Apart from the continuous advancements in technology, the world has continued to accept the need for remote work setting. After the COVID-19 breakout, the idea of remote working has not only been effective, but it has also shown a promising future due to the increased employee performance and satisfaction. However, remote work setting has also presented a few challenges that majorly depend on the type of leadership applied which also aligns with the needed work structure and culture.

When dealing with workers in a remote work setting, the style of leadership approach must capture various essential details that affect employee motivation, performance, and job satisfaction. Any employee working in a remote work setting was expected to experience several challenges which can vary depending on the individual’s personality and background. In most cases, employees would be expected to feel isolated, pressured, lack structure, and have difficulty in separating personal life and work (Chen, Liu, & Zhang, 2020). On the same note, workers in remote work setting have been described to have a lot of difficulties when it came to effective communication and collaborations among the management structure (Chen, Liu, & Zhang, 2020). These issues were a direct result of geographical difference and the aspect of facing various additional problems. In response to these challenges, most employees in remote workplace tend to feel unmotivated and unsatisfied with their work since everyone tends to lose interest in that common goal. In any work setting, attaining effective leadership can be quite challenging which makes it even more difficult when it comes to remote setting. It’s important to start by noting that leadership plays a very crucial role in promoting effective communication that translates to a proactive and productive workforce (Chen, Liu, & Zhang, 2020). Following the above comment, remote workplace creates a complicated scenario of which the absence of physical leadership has various challenges with lack of motivation, guidance, and most importantly support from one another. Leadership style has a significant connection with how employees view their work experience and how they find their place within an organization (Karim & Abbas, (2020). For instance, Participative and Transformational Leadership Styles have been commended on improving employee performance while at the same time increasing their job satisfaction (Karim & Abbas, (2020).

Despite having various challenges, remote working has shown very promising prospects in terms of employee performance, motivation, and satisfaction. According to the report from big IT companies such as Alphabet, Microsoft, Meta, Amazon, and others, workers have shown an increased sense of creativity while working in remote areas as compared when they were operating within their workstations (Karim & Abbas, (2020). This finding has forced most of these companies to review working policies with workers being allowed to work more remotely. While implementing such a plan, there must be full account of all features affecting employees where the style of leadership chosen must be able to offer each employee the needed inspiration and guidance. In relation to remote work setting, the instilled leadership style must cover each individual trait as it would highly depend on their reactions without being supervised (Chen, Liu, & Zhang, 2020).

Over the past few decades, there has been a number lot of research has been completed investigating the various styles of leadership and the context in which they work effectively. In general, leadership style can be described as an approach or structure used to direct or coordinate team or teams to achieve a common goal. Therefore, it is essential to note that leadership plays a crucial role in any organization providing employees with motivation, direction, and purpose of (achieving the organization’s mission and goals). According Araz & Azadegan-Mehr, (2021), for any leadership style to be considered effective, it must have a well-structured communication channel that allows smooth flow of information without or with minimal distortion. An effective leadership style must be reliable in terms of delivering messages while at the same time positively influencing employee’s attitude (Araz & Azadegan-Mehr, (2021). These aspects have been identified in the paper as one of the main features that must be considered (when choosing a leadership style for remote workers.

Remote work has become the new norm in many organizations due to the pandemic, and it has posed a challenge to leaders. Leaders must ensure that remote employees remain productive, motivated, and satisfied with their jobs. However, there was a gap in knowledge regarding the impact of leadership styles on remote employee outcomes. Thus, this study aims to address this gap by investigating the role of leadership styles on employee performance, motivation, and job satisfaction in a remote setting.

The purpose of this study was to investigate the impact of leadership styles on employee performance, motivation, and job satisfaction in a remote setting. This study aims to provide insights into how different leadership styles can affect remote employees and their job outcomes.

i. Employee Performance: The degree to which an employee performs their job responsibilities effectively (Allred et. Al., 2018).

ii. Job Satisfaction: The event to which an employee was content with their job and their work environment (Allred et. Al., 2018).

iii. Leadership: The process of influencing and guiding others towards a common goal (Chen, Liu, & Zhang, 2020).

iv. Leadership Styles: Refers to the different ways leaders interact with their subordinates to accomplish organizational goals (Chen, Liu, & Zhang, 2020).

v. Motivation: The internal and external factors that drive individuals to achieve their goals (Allred et. Al., 2018).

vi. Remote Leadership: The ability to manage and guide remote employees towards achieving their goals in a virtual work environment. It requires a unique set of skills to effectively manage remote teams, such as strong communication, trust-building, and the ability to adapt to different cultural and geographical contexts (Chen, Liu, & Zhang, 2020).

vii. Remote Work: Refers to a work arrangement where employees do not work from a physical office but instead work from their homes or other remote locations. This arrangement was made possible using technology and communication tools (Chen, Liu, & Zhang, 2020).

Herzberg’s two-factor theory was the primary theoretical framework for this study. Herzberg et al. (1967) noted two factors of motivation and those factors enhanced motivation and job performance if present or decreased performance and motivation if absent. These two sets of factors were called hygiene and motivation factors (Yusoff et al., 2013). Hygiene factors was related to working conditions and environments such as salary, benefits, interpersonal relationships, and company policies, whereas motivators were connected to work itself such as recognition, responsibility, achievement, and self-development opportunity (Hur, 2018). Yusoff et al. (2003) stated that hygiene factors affected employee dissatisfaction while motivation factors increased employee job satisfaction. The implication for an organization using this theory was that employee hygiene factors being met prevented employees from becoming dissatisfied but did not motivate them to contribute more effort in better job performance (Yusoff et al., 2013) and with increased job performance, motivation factors needed to be addressed (Kermally, 2005). Yusoff et al. (2013) noted that extrinsic (hygiene) factors guided employers in creating a favorable work environment where employees feel comfortable working inside the organization. These factors promoted work satisfaction and included a safe and clean working environment (Kermally, 2005). When all the extrinsic factors were achieved, employees were free from unpleasant outside working conditions that removed dissatisfied feelings (Yusoff et al., 2013). Kermally (2005) mentioned that if working conditions were inadequate, supervision was poor or unsafe conditions were present, reduced job performance and dissatisfaction likely arose from employees. With increased job performance, motivation factors should be addressed (Kermally, 2005). Motivation factors include a sense of achievement, job interest, advancement, and responsibility (Kermally, 2005). Yusoff et al. (2013) mentioned that those motivation (intrinsic) factors were influential in establishing and maintaining positive effects on employee performance towards their job as this factor became a basic human need for psychological growth. Kermally (2005) stated hygiene factors needed to be attended to first to make sure motivation was not decreased, then focus was put on motivating factors.

The following research questions and hypotheses would guide this study:

RQ1: Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

This study focuses on the role of leadership styles on employee performance, motivation, and job satisfaction in a remote setting. The study would be conducted using instruments to collect data from remote employees. The study would be focused on employees across the United States.

This study was significant because it would provide insights into the role of leadership styles in managing remote employees. The findings of this study can help organizations to develop effective leadership strategies to improve remote employee outcomes.

It’s important to note that effective leadership style was also highly dependent on the organization structure and culture irrespective of whether it’s operating physically or remotely. The type of study the researcher would be using was the Quantitative study. Apart from only offering direction, an effective leadership style must set a tone for the organization by establishing a clear vision, mission, goals, and values that help it to thrive (Allred et. Al., 2018). Therefore, the alignment of leadership style, organizational structure, and culture was significantly important in achieving employee’s performance, motivation, and 5satisfaction. Depending on the organizational structure and culture, effective leadership style has to maintain control and ensure compliance with the rules and regulations.

According to Den Hartog and Koopman (2001), leadership was a crucial aspect of organizational management, and it plays a significant role in determining employee motivation, job satisfaction, and organizational culture. A leader’s behavior, actions, and communication style can significantly influence these three factors. First, leadership affects employee motivation. Leaders can inspire, motivate, and encourage their followers to achieve their goals (Amabile et al., 2004). Effective leaders can create a shared vision and mission that fosters a sense of purpose and belonging among their employees. By doing so, leaders can create a sense of ownership and accountability among their employees, which can increase their motivation to perform their job duties to the best of their abilities.

Second, leaders who are supportive, approachable, and willing to listen to their employees can foster a positive work environment, thus impacting job satisfaction. Leaders who recognize and appreciate their employees’ efforts and contributions can help to create a culture of recognition and appreciation. This type of culture can increase employee job satisfaction and create a sense of belonging within the organization. Finally, leadership affects organizational culture. The behavior and actions of leaders can significantly influence the organizational culture. Leaders who promote open communication, collaboration, and teamwork can foster a culture of innovation and creativity (Den Hartog & Koopman, 2001). In contrast, leaders who were hierarchical, controlling, and resistant to change can stifle creativity and create a negative culture.

Several factors influence leadership, and they include the leader’s personality, the followers’ expectations, the organizational culture, and the external environment (Van Knippenberg et al., 2004). The leader’s personality plays a crucial role in shaping the leadership style that they employ. For instance, authoritarian leaders tend to be dominant, assertive, and controlling. On the other hand, democratic leaders tend to be sociable, friendly, and approachable. The followers’ expectations also influence the leadership style that a leader employs. Leaders who were appointed to lead a team with experienced and skilled followers may adopt a democratic leadership style because they recognize that their followers have valuable contributions to make.

A study conducted by O’Reilly and Chatman (1996) established that organizational culture also plays a significant role in shaping leadership. Leaders who were appointed to lead organizations with a bureaucratic culture may adopt an authoritarian leadership style, and adherence to rules and regulations. On the other hand, leaders who were appointed to lead organizations with a flexible culture may adopt a laissez-faire or transformational leadership style because the culture values innovation and creativity. The external environment also influences leadership. According to Goleman (2000), leaders who operate in a stable environment may adopt a laissez-faire leadership style because the environment was predictable, and there was little need for supervision. On the other hand, leaders who operate in a volatile environment may adopt an authoritarian leadership style because the environment requires quick decisions and decisive action.

As with leadership styles, leadership theories were of importance to leaders. These theories aim to explain the nature of leadership, how it evolved, and how it was practiced. Numerous theories have been proposed over the years, and they can be broadly categorized into trait, behavioral, contingency, transformational, and situational theories. Trait theories propose that leadership was a function of an individual’s inherent traits, such as intelligence, self-confidence, and assertiveness (Lussier & Achua, 2015). These theories suggest that individuals with certain characteristics were more likely to emerge as leaders and were better suited to leadership roles. Early trait theories focused on identifying specific personality traits that were associated with effective leadership. For example, the “Great Man” theory proposed that leaders were born, not made, and that they possessed innate qualities such as intelligence, charisma, and confidence (Dinh et al., 2014). Later trait theories focused on identifying broader categories of traits, such as the Big Five personality traits (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience).

In comparison, behavioral theories focus on identifying the specific behaviors that effective leaders exhibit. These theories propose that leadership was not based on inherent traits but rather on learned behaviors. The Ohio State University studies identified two dimensions of leader behavior: initiating structure (the extent to which a leader defines and organizes tasks and roles) and consideration (the extent to which a leader shows concern for the well-being and personal needs of employees) (Amabile et al., 2004). Another influential behavioral theory was the contingency theory of leadership, which proposes that effective leadership depends on the situation in which it was practiced (Dinh et al., 2014).

Contingency theories were also critical and propose that effective leadership was contingent upon the specific situation in which it was practiced. These theories suggest that different situations require different types of leadership behaviors or styles. The most well-known contingency theory was the situational leadership theory, which proposes that leaders should adjust their leadership style to the development level of their followers. The path-goal theory of leadership was another contingency theory that suggests that leaders should provide guidance and support to employees to help them achieve their goals (Dinh et al., 2014).

Transformational theories have gained the attention of various authors in the recent past. These theories propose that leadership was a process of inspiring and motivating followers to achieve their full potential. They suggest that effective leaders were those who can articulate a vision and inspire others to work towards it. The transformational leadership theory proposed by James MacGregor Burns (1978) suggests that effective leaders were those who can inspire followers to transcend their self-interests and work towards a shared vision. Bass and Avolio (1994) later developed the full range model of transformational leadership, which includes four components: idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration.

Another set of theories entails situational theories, which propose that leadership effectiveness depends on the specific situation in which it was practiced. These theories suggest that leaders must adapt their leadership style to the situation to be effective. The situational leadership theory proposed by Hersey and Blanchard (1982) suggests that leaders should adjust their leadership style to the development level of their followers. The leader-member exchange theory was another situational theory that proposes that leaders develop unique relationships with each of their followers, and that the quality of these relationships affects leadership effectiveness (Lussier & Achua, 2015).

Without a doubt, effective leadership has a significant impact on organizational success. Effective leaders can improve the organization’s performance by setting goals, inspiring and motivating employees, promoting innovation and creativity, and fostering a positive work environment. According to Den Hartog and Koopman (2001), leaders who can create a positive work environment were more likely to have satisfied employees who were loyal to the organization. A positive work environment fosters collaboration, teamwork, and mutual respect, which leads to increased productivity and job satisfaction as established by Van Knippenberg et al. (2004).

Additionally, leaders who can inspire and motivate employees can create a sense of purpose and direction for the organization as highlighted by Goleman (2000). This can lead to increased employee engagement, which can improve organizational performance. Effective leaders also promote innovation and creativity, which can lead to new ideas and products that can help the organization stay competitive. Leaders who promote innovation and creativity were more likely to have employees who were willing to take risks and try new things (Den Hartog & Koopman, 2001).

Various researchers have studied leadership development and training, which has been established to be critical to the success of organizations. Effective leaders were needed to motivate and inspire employees, create a positive organizational culture, and achieve strategic goals (O’Reilly & Chatman, 1996). Considering the significance of leadership development and training, research has shown that effective leaders can positively impact employee engagement, job satisfaction, and organizational performance (Dinh et al., 2014). However, many organizations struggle to develop effective leaders. A lack of leadership development and training can result in low employee morale, high turnover rates, and a decline in organizational performance (Lussier & Achua, 2015).

There were various approaches to leadership development and training. Some organizations use internal development programs, while others hire external consultants to provide training. Research has shown that the most effective leadership development programs were those that were tailored to the individual needs of the leader. Additionally, effective leadership development programs should be based on evidence-based practices and should involve ongoing feedback and coaching (Dinh et al., 2014).

For both traditional and remote work settings, research has shown that leadership development and training can have a positive impact on employee engagement, job satisfaction, and organizational performance as documented by Lussier and Achua (2015). Effective leadership development programs can also lead to increased innovation and creativity within the organization. However, the impact of leadership development and training can vary depending on the specific program and the individual leader. Despite the importance of leadership development and training, there were many challenges that organizations face in implementing effective programs. One of the main challenges was the cost of leadership development and training programs (Lussier & Achua, 2015). Additionally, some organizations struggle to identify the most effective approaches to leadership development and training.

In relation to the need to attain optimum results, it was paramount to evaluate the effectiveness of leadership development and training programs before implementation. Leadership development and training programs were designed to improve the knowledge, skills, and abilities of individuals in leadership roles. The aim was to enhance leadership effectiveness, productivity, and organizational outcomes. The effectiveness of these programs can be evaluated based on various factors, including the program’s content, delivery, and outcomes. Firstly, the content of the program was a critical factor in evaluating its effectiveness. The program should be designed to meet the specific needs of the participants, be based on current research and best practices, and cover a wide range of topics related to leadership. The content should also be relevant to the participants’ roles and responsibilities.

Secondly, the delivery of the program was crucial. The program should be delivered in a manner that was engaging, interactive, and tailored to the participants’ learning styles. The delivery method could include a combination of classroom sessions, online modules, simulations, and coaching sessions. The program should also provide opportunities for participants to practice their skills and receive feedback from their peers and instructors. Moreover, the outcomes of the program should be evaluated to determine its effectiveness. The outcomes could include improved leadership skills, increased productivity, improved employee engagement, and retention rates. The evaluation should be conducted at different intervals, such as immediately after the program, six months, and one year after completion. Studies have shown that leadership development and training programs can be effective in improving leadership skills, increasing productivity, and improving employee engagement and retention rates. For instance, a study by the Center for Creative Leadership found that leadership development programs improved participants’ self-awareness, interpersonal skills, and ability to manage and lead teams effectively.

The advent of remote work, accelerated by the COVID-19 pandemic, has brought a new dimension to leadership research. Leaders in remote settings face a unique challenge in ensuring that employees remain motivated, productive, and satisfied. Notably, remote work settings were unique because they lack face-to-face communication, which can lead to a lack of trust, communication, and collaboration (Kirkman, Rosen, Gibson, Tesluk, & McPherson, 2002). In a remote work setting, leaders need to create a sense of community and maintain open communication channels to ensure employee engagement and productivity. As organizations adopt remote work, leaders must be able to adjust their leadership styles to suit the remote work environment.

The study conducted by Chen, Liu, and Zhang (2020) indicates that leadership was an essential component of organizational success, and effective leadership was even more critical in a remote setting. In such a setting, leaders must also be able to adapt to the challenges and opportunities presented by the virtual work environment. However, it was paramount for leaders to grasp an understanding of these associated opportunities and challenges. According to Gajendran and Harrison (2007), remote work provides many opportunities for both employees and employers in the sense that it promotes flexibility. Employees can work from anywhere, and employers can benefit from a workforce that was not limited to a specific geographical area. This flexibility can also reduce stress levels and increase job satisfaction, which can translate into increased productivity (Gajendran & Harrison, 2007). Remote work also reduces operational costs for organizations, such as rent, utilities, and office supplies. This cost-saving allows companies to invest in other areas, such as employee training and development. Additionally, remote work allows organizations to access a more diverse pool of talent, regardless of geographic location (Bughin et al., 2018). Remote work also presents opportunities for work-life balance. With remote work, employees can better manage their personal responsibilities, such as childcare or caring for elderly parents. This flexibility can also reduce the likelihood of burnout, which was a significant problem in the workplace (Moen et al., 2015).

While remote work presents many opportunities, it also presents various challenges. One of the significant challenges was communication. Golden et al. (2020) document that remote work can create communication barriers, such as time zone differences, language barriers, and technological issues, which potentially lead to misunderstandings, delays, and reduced productivity. Another significant challenge was isolation. Remote workers may feel isolated from the rest of the team, leading to feelings of loneliness and disengagement (Kim & Wind, 2020). This can also lead to reduced collaboration and difficulty in building relationships with colleagues. Managing remote workers was also a challenging task. According to McGregor and Harris (2018), leaders and managers may struggle to manage the performance of remote workers and may find it difficult to provide feedback. Additionally, remote workers may face difficulties in balancing their work and personal lives, leading to them being workaholics and increasing employee burnout (Golden et al., 2020).

Leaders were mandated to address the challenges of remote working to reap maximized benefits and attain desirable outcomes. They must provide clear communication and guidance to their employees, build trust and rapport, and foster a sense of community among the team. Research has shown that effective communication was critical in remote work environments (Golden et al., 2020). Leaders must be able to communicate effectively through various virtual channels such as video conferencing, instant messaging, and email. Furthermore, leaders must also be able to address the unique challenges faced by remote workers, such as feelings of isolation and lack of support. The role of leadership in promoting employee well-being and mental health in a remote setting was crucial (Golden et al., 2020). Leaders must be able to provide support and resources to their employees to promote well-being and prevent burnout. Without a doubt, the role of leadership styles in a remote setting cannot be overemphasized.

Leadership was a multifaceted phenomenon that has been studied by researchers from different perspectives over the years. Various authors argue that leadership style was an essential factor that can influence employee behavior, performance, motivation, and job satisfaction in a remote setting (Chen, Liu, & Zhang, 2020; Goleman, 2000). According to Goleman (2000), there were six leadership styles: coercive, authoritative, affiliative, democratic, pacesetting, and coaching. These leadership styles have been linked to significant inputs and outcomes in the traditional setting, despite the minimal drawbacks. For starters, coercive leadership style entails the use of threats, punishments, and force by leaders to get their followers to comply with their instructions. It was often used in situations where quick and decisive action was required, or in situations where there was a high level of uncertainty or risk. While coercive leadership can be effective in certain situations, it can also be damaging to organizations and their followers, particularly if it was applied in a remote work setting as documented by Kelloway, Francis, and Gatien (2012).

One of the key concerns of coercive leadership its impact on the psychological well-being of individuals. Research suggests that leaders who rely on coercive tactics can create an environment of fear and anxiety, leading to reduced job satisfaction, increased stress, and decreased commitment among followers (Kelloway et al., 2012). This can also lead to increased turnover and absenteeism, as well as a decline in productivity and performance (García-Morales, Jiménez-Barrionuevo, & Gutiérrez-Gutiérrez, 2012). Moreover, coercive leadership can also create a toxic organizational culture that was focused on compliance rather than collaboration and innovation (Pearce & Conger, 2003). This can hinder creativity and hinder progress towards organizational goals. On the other hand, some studies have found that in certain situations, such as during times of crisis, coercive leadership can be an effective means of managing the situation and achieving the desired outcome (Bass & Riggio, 2006). In addition to the impact on individuals and organizations, research has also explored the factors that influence the use of coercive leadership. One key factor was the leader’s personality traits, such as their need for control and their level of aggression (Zhang & Bednall, 2016). Another factor was the organizational culture and its tolerance for authoritarian leadership styles (García-Morales, Jiménez-Barrionuevo, & Gutiérrez-Gutiérrez, 2012).

Authoritative leadership style has proved to be significant in both the traditional and remote work environments since the involved leaders who use this style provide clear direction and goals and allow their followers to exercise their own creativity and initiative in achieving the set goals. This leadership style was characterized by a focus on the big picture, a vision for the future, and a willingness to take risks. Research suggests that authoritative leadership can have positive effects on organizations and employees, even those in the remote work setting. Leaders who adopt an authoritative style tend to have a clear vision for the future and can communicate that vision effectively to their followers, creating a sense of purpose and direction (Den Hartog & Koopman, 2001). This can lead to increased motivation, job satisfaction, and commitment among followers (Xenikou & Simosi, 2006).

Moreover, authoritative leadership can also lead to higher levels of innovation and creativity in remote work settings (Amabile, Schatzel, Moneta, & Kramer, 2004). By giving followers, the freedom to exercise their own creativity and initiative, leaders can tap into the unique talents and perspectives of their followers and encourage them to take risks and try new things. In addition to the positive effects on individuals and organizations, research has also explored the factors that influence the use of authoritative leadership. One key factor was the leader’s level of expertise and knowledge in their area of work (Yukl, 2010). Leaders who have a high level of expertise were better able to provide direction and guidance to their followers, while also allowing them to exercise their own creativity and initiative. Another factor was the organizational culture and its emphasis on innovation and risk-taking (Xenikou & Simosi, 2006). Organizations that value innovation and creativity were more likely to adopt an authoritative leadership style, as it allows for the exploration of new ideas and the pursuit of ambitious goals. For authoritative leaders in a remote setting, leaders were mandated to strike a balance between providing clear direction and allowing their followers to exercise their own creativity and initiative, which can be achieved by creating a culture of trust, collaboration, and open communication.

Affiliative leadership was another leadership style of significance in any work setting, where the leaders prioritize building positive relationships with their followers. This leadership style emphasizes creating a supportive and collaborative work environment that fosters trust, open communication, and teamwork. In correlation to this, affiliative leadership style has been adapted by leaders in the remote work setting. Such leaders tend to create a supportive and nurturing work environment that fosters trust, open communication, and teamwork (Goleman, Boyatzis, & McKee, 2013). This can lead to increased job satisfaction, motivation, and commitment among followers (Bass & Riggio, 2006). Moreover, affiliative leadership also promotes invention as established by Amabile, Schatzel, Moneta, and Kramer (2004). By creating a supportive work environment that encourages open communication and collaboration, leaders can tap into the unique talents and perspectives of their followers and encourage them to take risks and try new things.

Additionally, research has also explored the factors that influence the use of affiliative leadership. One key factor was the leader’s personality and interpersonal skills (Bass & Riggio, 2006). Leaders who were empathetic, supportive, and good listeners were more likely to adopt an affiliative leadership style. Another factor was the organizational culture and its emphasis on collaboration and teamwork (Goleman, Boyatzis, & McKee, 2013). Organizations that value collaboration and teamwork were more likely to adopt an affiliative leadership style, as it fosters a sense of unity and collective effort. Overall, while affiliative leadership can have positive effects on individuals and organizations, it was important for leaders to balance the need for collaboration and support with the need for clear direction and decision-making. This can be achieved by creating a culture of trust, open communication, and shared decision-making.

Democratic leadership style was another leadership style that has gained the attention of researchers and has been established to be one of the widely applied styles across organizations. This style emphasizes collective decision-making, participation, and involvement of all members in the decision-making process. In this approach, leaders act as facilitators, encouraging their team members to share their ideas and perspectives, leading to better decision-making, team building, and job satisfaction. A study conducted by Avolio and Gardner (2005) on the impact of democratic leadership on work-related attitudes and behaviors found that employees working under democratic leaders were more satisfied with their jobs and demonstrated higher levels of performance compared to those working under authoritarian or laissez-faire leaders. Another study conducted by House and Aditya (1997) found that democratic leadership had a positive impact on employee motivation and job satisfaction, leading to higher levels of productivity and profitability in the organization. Moreover, research has also shown that democratic leadership can have a positive impact on employee productivity. A study conducted by Eisenbeiss, Knippenberg, and Boerner (2008) found that teams working under democratic leaders demonstrated higher levels of creativity and innovation compared to those working under authoritarian leaders. This was because democratic leaders foster an environment that encourages the sharing of diverse perspectives and ideas, leading to a more innovative and creative work culture.

However, democratic leadership was not without its limitations. One potential drawback of this approach was that it can be time-consuming, as it involves a collective decision-making process that may take longer than a unilateral decision-making process. Additionally, research has shown that democratic leadership may not be suitable in situations where quick decision-making was essential, such as in emergency or crisis situations. With this understanding, leaders must be aware of its limitations and carefully assess the situation before implementing this approach. Researchers should also aim at addressing the gap pertaining to the impact of democratic leadership style in remote work setting.

Unlike other leadership styles, the pacesetting and coaching styles of leadership have been the subject of much attention and discussion in the recent past. The pacesetting style of leadership emphasizes setting high standards and goals for followers, with the expectation that they would work hard to achieve them. Leaders who adopt this style of leadership expect their followers to meet or exceed their expectations, and they often take a hands-on approach to ensure that these standards were met. This leadership style was effective in certain situations, such as when dealing with highly motivated and skilled employees or when there was a need for quick results. However, research has shown that the Pacesetting style can have negative consequences on employee well-being and motivation. A study conducted by Goleman et al. (2002) found that the Pacesetting style was the least effective leadership style and had the most negative impact on employee performance and job satisfaction. This was because the Pacesetting style can lead to burnout and high turnover rates among employees, as they struggle to keep up with the high standards set by their leader.

On the other hand, the coaching style of leadership involves a supportive and developmental approach to leadership, aimed at helping followers reach their full potential. Leaders who adopt this style of leadership provide guidance, feedback, and support to their followers, helping them develop the skills and abilities they need to succeed. This leadership style was effective in improving employee performance, motivation, and job satisfaction, particularly in a remote work setting. A study conducted by Grant and Hartley (2013) found that leaders who adopted a Coaching style had more engaged and committed employees, resulting in higher levels of productivity and job satisfaction. The reason for this outcome was because the coaching style creates an environment where employees feel valued and supported, leading to increased job satisfaction and motivation. Moreover, research has shown that the Coaching style was effective at developing the skills and abilities of employees. A study by Boyatzis and McKee (2005) found that leaders who used the Coaching style were more effective at developing the skills and abilities of their employees, leading to improved performance and career advancement opportunities. Despite the effectiveness of the coaching style, it was important to note that the leadership style has its limitations. This style can be time-consuming and may not be suitable for situations that require quick decision-making. Moreover, leaders who adopt this style must be skilled at providing constructive feedback and guidance, as it can be challenging to strike a balance between support and micromanagement.

Both the pacesetting and coaching styles of leadership have their strengths and limitations. The Pacesetting style can be effective in certain situations, but it should be used sparingly to avoid negative consequences on employee well-being and motivation. The Coaching style, on the other hand, was effective in improving employee performance, motivation, and job satisfaction, but leaders must be skilled at providing constructive feedback and guidance to their followers.

Considering the different leadership styles and the associated impact, a style can be effective in one work setting or situation and fail in another. In relation to this, some leadership styles hardly yield results when applied in a remote work setting, even though it has been tried and tested in the traditional setting. A study by Wang and Huang (2020) found that while affiliative leadership style positively affects employee job satisfaction in a remote work setting, its impact in the traditional setting was far much more tangible with significant outcomes. Similarly, Chen et al. (2020) found that democratic leadership style positively affected employee job satisfaction and motivation in a remote work setting, with maximized output experienced in a traditional work setting. In the context of the remote work setting, additional leadership styles would be considered, including structural, participative, servant, freedom-thinking, and transformational.

According to Robbins and Judge (2017), Structural leadership was a leadership style that emphasizes strict adherence to rules and regulations. Structural leaders were generally known for their expertise in organizing and developing efficient and effective systems and structures within the organization. Such structures were typically characterized by clear lines of authority, precise job descriptions, and formalized procedures. In this same context, Bass (1985) documents that structural leadership styles were characterized by a high degree of control and direction by the leader. While it’s a leadership style on its own, it has subsets of applicable styles, including the autocratic leadership style, where the leader makes all decisions without any input from the followers. Autocratic leaders tend to have a low level of trust in their followers and use their power and authority to enforce their decisions (Bass, 1985). This style was effective in emergency situations that require quick decisions, but it can lead to employee dissatisfaction, resistance, and high turnover rates in the long run (Hassan & Ahmed, 2011).

Bureaucratic leadership was also considered a structural leadership style. In this style, the leader follows the rules and procedures to the letter, with little to no room for creativity or deviation from the established norms. Bureaucratic leaders tend to prioritize the maintenance of the status quo over innovation and experimentation (Bass, 1985). This style was effective in situations where consistency and predictability were essential, such as in financial institutions or government agencies. Another structural leadership style was Laissez-faire leadership, in which the leaders provide little to no guidance or direction to their followers, leaving them to work independently. Laissez-faire leaders tend to be hands-off and trust their followers to make the right decisions. This style can be effective in situations where the followers were highly skilled and self-directed, such as in academic research, but it can lead to a lack of accountability and direction in the long run (Bass, 1985).

Research has shown that structural leadership style can be effective in different situations, having both positive and negative effects on employee performance, motivation, and job satisfaction. For example, Autocratic leadership can be effective in emergency situations that require quick decisions, while Bureaucratic leadership can be effective in situations where consistency and predictability was essential. Notably, structural leadership style can lead to improved organizational efficiency and productivity. O’Reilly and Chatman (1996) establish that structural leaders often have a clear understanding of the organization’s goals, which allows them to develop processes that enable employees to work more efficiently.

However, on the other hand, structural leadership can also adversely impact employee motivation and job satisfaction. Research has shown that strict adherence to rules and regulations can lead to a lack of autonomy and a sense of micromanagement, which can lead to decreased job satisfaction (Van Knippenberg, Van Knippenberg, De Cremer, & Hogg, 2004). Employees may also become disengaged when they feel that their contributions were not valued or when they feel that their input was not sought after. Moreover, research has shown that structural leadership style was more effective in certain organizational contexts than others. In organizations with complex procedures and regulations, the structural leadership style can be more effective (Robbins & Judge, 2017). However, in organizations with more fluid and dynamic environments, the structural leadership style may be less effective, as it may not provide the flexibility needed to adapt to changes in the environment.

In a remote work setting, the application of structural leadership yields beneficial results. A study conducted by Barling et al. (2015) established that structural leadership was positively related to employee performance, as it provided employees with a clear sense of direction and focus. The study also found that the use of rules and procedures helped remote employees to stay on track and meet their goals. Structural leadership also impacts employee engagement, which was an essential aspect of employee motivation and commitment. According to a study by Graham et al. (2019), structural leadership styles can positively impact employee engagement in a remote work setting, addressing the issues where employees feel isolated. The study found that the use of clear guidelines and expectations helped remote employees feel more connected to their work and their organization. Other relevant impacts of structural leadership were associated with increased employee satisfaction and reduced employee burnout in a remote work setting. According to a study by O’Boyle Jr. et al. (2015), the use of clear guidelines and expectations helped remote employees feel more satisfied with their work and their organization. The study also found that the use of rules and procedures helped remote employees to feel more in control of their work, which contributed to their overall satisfaction. Structural leadership styles can also help prevent employee burnout in a remote work setting (Sonnentag et al, 2012). The study found that the use of rules and procedures helped remote employees to manage their workload effectively, which reduced their risk of burnout.

Participative leadership style includes involving subordinates in the decision-making process, seeking input, and encouraging collaboration among team members. The virtual nature of remote work requires leaders to employ more explicit communication and actively seek input from team members. Research has shown that participative leadership style can have a positive impact on employee performance, motivation, and job satisfaction in remote work settings. A study conducted by Araz and Azadegan-Mehr (2021) found that participative leadership style increased team performance and job satisfaction in virtual teams. Additionally, the study showed that participative leadership style positively affected employee motivation, leading to a greater sense of engagement in virtual teams. Moreover, a study conducted by Breevaart, Bakker, Hetland, Demerouti, and Olsen (2016) found that participative leadership style in remote work settings improved employee job satisfaction, mainly due to increased autonomy and job control. In support of this, Zhang et al. (2020) established that participative leadership promotes enhanced employee performance and innovation in a virtual team environment.

Also, a study by Maertz et al. (2021) found that participative leadership styles were positively associated with employee engagement, job satisfaction, and organizational commitment in a remote work setting. Another study by Ehrhart et al. (2020) found that participative leadership styles were positively associated with employee psychological safety in a remote work setting, through which it reflects positively on employee engagement and team performance. Furthermore, a study by Chou et al. (2021) found that participative leadership style was positively associated with employee trust in a remote work setting. The study also found that participative leadership styles had a significant positive impact on employee task performance, innovation, and job satisfaction in a virtual team environment.

There has been a growing interest in the servant leadership style, which emphasizes serving the needs of employees and promoting their personal and professional development. In remote work settings, servant leaders prioritize the needs of their team members and work to create an environment that fosters collaboration, trust, and open communication. With various studies conducted on servant leadership, its significance pertaining to employee motivation, job satisfaction, and performance in remote work settings has been established. A study conducted by Kim, Lee, and Lee (2021) found that servant leadership was positively associated with job satisfaction and employee motivation in virtual teams. The study also found that servant leadership had a significant positive effect on employee performance.

In remote work settings, a study by Nielsen, Marrone, and Ferris (2017) found that servant leadership in remote work settings was associated with higher levels of team commitment and trust, which in turn led to increased job satisfaction and motivation. The study also found that servant leadership had a positive impact on team performance. Due to a servant leader’s emphasis on empathy and listening skills, servant leadership helps alleviate the feelings of isolation and disconnection. Proper implementation of these leadership styles ascertains that leaders were better positioned to build trust with their employees by showing genuine concern for their well-being and creating a safe space for open communication. As documented Sendjaya et al., 2008, the developed trust can lead to higher job satisfaction and better performance from employees. Additionally, servant leadership promotes a sense of belonging among remote employees. Servant leaders prioritize collaboration and teamwork, which can help remote employees feel more connected to their colleagues. In this same context, servant leaders foster a culture of inclusivity, which can help to create a sense of belonging for employees from diverse backgrounds (Sendjaya et al., 2008).

Liden et al. (2008) also argue that servant leadership enhances employee motivation and engagement, mainly since leaders using this style tend to empower their employees by providing them with the necessary resources and support to excel in their roles. This empowerment ascertains that subordinates feel valued and supported by their leaders, thus reflecting positively on employee engagement and motivation. Furthermore, servant leaders provide their employees with opportunities for personal and professional growth, which can lead to increased job satisfaction and loyalty to the organization (Liden et al., 2008). According to Sendjaya et al. (2008), servant leadership promotes a culture of accountability and responsibility, which can help to create a sense of ownership among employees, leading to higher levels of productivity and quality of work (Sendjaya et al., 2008).

Freedom-thinking leadership style emphasizes empowering employees to take ownership of their work and providing them with the freedom to make decisions and explore new ideas. In remote work settings, this style can be particularly effective as it allows employees to work independently while still feeling supported and valued. Research has shown that freedom-thinking leadership can have a positive impact on employee creativity, job satisfaction, and performance in remote work settings. A study conducted by Karim and Abbas (2020) found that freedom-thinking leadership was positively associated with employee creativity in remote work settings. The study also found that freedom-thinking leadership had a significant positive effect on employee job satisfaction. Additionally, a study by Allred et al. (2018) found that freedom-thinking leadership in remote work settings was associated with increased employee performance. The study also found that this leadership style had a positive impact on employee job satisfaction. Zhou et al. (2019) found that freedom-thinking leadership was positively associated with employee innovative behavior in remote work settings. The study also found that this leadership style had a positive impact on employee job satisfaction.

In a remote setting, this style of leadership can be particularly effective as it helps to build trust and foster a sense of community among team members. It also promotes increased employee engagement. A study by Gallup (2017) found that employee engagement was lower among remote workers than their in-office counterparts. However, leaders who embrace freedom-thinking can help to mitigate this issue by providing employees with the tools and resources they need to be successful, and by empowering them to make decisions and take ownership of their work (Allred et al. (2018). When employees feel like they have control over their work and are trusted to make decisions, they are more likely to be engaged and motivated. Another benefit of freedom-thinking leadership in a remote setting was improved communication. Remote working arrangements can make communication more challenging, but leaders who embrace this style can overcome these barriers by encouraging open and transparent communication as documented by Zhou et al. (2019). By empowering employees to speak up and share their ideas, leaders can create a culture of trust and collaboration that helps to drive innovation and problem-solving. However, it was worth noting that freedom-thinking leadership was not without its challenges. For example, leaders must strike a balance between providing autonomy and maintaining accountability. When employees have too much freedom, it can be difficult to ensure that they were meeting their goals and producing quality work. Leaders must also be careful to avoid micromanaging, as this can undermine the trust and autonomy that was the hallmarks of this style of leadership.

Another relevant leadership style applicable in a remote setting was transformational leadership. This leadership style focuses on inspiring and motivating employees to achieve their goals and aspirations. Also, it emphasizes the importance of empowering employees and creating a supportive and collaborative work environment. In most instances, the transformational leader serves as a role model for their followers and encourages them to transcend their self-interest for the benefit of the organization. This style of leadership has gained considerable attention from researchers and practitioners due to its positive impact on employee motivation, job satisfaction, and organizational performance (Bass & Riggio, 2006). Bass and Riggio (2006) argue that this leadership style was defined by four key elements, which reflect heftily on the outcomes. These elements, otherwise referred to as the 4 I’s include idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration (Bass & Riggio, 2006). Idealized influence refers to the leader’s ability to serve as a role model for their followers, inspiring them to emulate their behavior and values while inspirational motivation involves the leader’s ability to articulate a compelling vision and inspire their followers to achieve higher levels of performance. Intellectual stimulation involves the leader’s ability to challenge their followers to think creatively and critically, promoting innovation and learning. Finally, individualized consideration involves the leader’s ability to provide personalized support and recognition to their followers, considering their individual needs and strengths (Bass & Riggio, 2006).

According to Avolio, Zhu, Koh, and Bhatia (2004), transformational leadership was associated with higher levels of job satisfaction and organizational commitment among employees. Furthermore, transformational leaders were more likely to promote a positive work environment, where employees feel supported, recognized, and empowered. This positive work environment can lead to higher levels of employee engagement and productivity, as well as lower turnover rates (Bass & Riggio, 2006). Transformational leadership has also been found to have a positive impact on organizational performance, including financial performance, innovation, and customer satisfaction. According to Jung, Wu, and Chow (2008), transformational leadership was associated with higher levels of organizational innovation, as well as higher levels of customer satisfaction. Furthermore, transformational leaders were more likely to promote a culture of excellence and continuous improvement, leading to higher levels of organizational performance and competitiveness (Bass & Riggio, 2006).

In remote work settings, transformational leaders use technology to maintain communication and build relationships with employees, leading to increased trust and engagement. A study by van der Velden et al. (2020) found that transformational leadership was positively associated with employee job satisfaction and performance in remote work settings. The study also found that transformational leadership had a significant positive effect on employee motivation. Huang et al. (2020) also found that transformational leadership in remote work settings was positively associated with employee creativity. The study also found that transformational leadership had a positive impact on employee job satisfaction. Zhu et al. (2020) found that transformational leadership was positively associated with employee well-being in remote work settings. The study also found that transformational leadership had a positive impact on employee job satisfaction and engagement. Furthermore, a study by Liao, Liu, and Liu (2017) found that transformational leadership style positively affected employee job satisfaction and performance in a remote work setting. Transformational leaders inspire and motivate employees to achieve their full potential, which can lead to increased employee satisfaction and performance.

In conclusion, leadership styles were of significance in shaping the success of organizations in a remote work setting. The changing nature of work has forced organizations to embrace remote work, and leaders must adapt to the new reality to achieve organizational goals. The literature indicates that different leadership styles can have varying impacts on remote employees and overall organizational performance. Transformational leadership can be effective in motivating remote employees, enhancing job satisfaction, and improving organizational performance. Transformational leaders who exhibit idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration can inspire remote employees to achieve higher levels of performance and engagement. They can also create a positive work environment that fosters innovation, learning, and continuous improvement.

While the leadership trajectory contributes towards the outcomes, communication was an essential factor that influences the effectiveness of leadership styles in a remote work setting. A study by Kim and Beehr (2020) found that communication quality mediated the relationship between leadership styles and employee job satisfaction in a remote work setting. In other words, leaders who communicate effectively and frequently can enhance the positive effects of their leadership styles on employee job satisfaction, regardless of the leadership style applied. Another important factor that can influence the effectiveness of leadership styles in a remote work setting was the level of autonomy provided to employees.

The literature also points out the need for leaders to embrace technology and leverage it to achieve organizational goals in remote work settings. Leaders who use technology effectively can facilitate communication, collaboration, and knowledge sharing among remote employees. They can also use technology to monitor employee performance and ensure accountability. Without a doubt, it was important for leaders to adopt a leadership style that was appropriate for the situation and the needs of their followers. In some cases, a hands-on approach may be necessary, while in others, a supportive and developmental approach may be more effective. By understanding the strengths and limitations of different leadership styles, leaders in remote work settings can create a work environment that fosters employee well-being, motivation, and performance.

Apart from the continuous advancements in technology, the world has continued to accept the need for remote work setting. After the COVID-19 breakout, the idea of remote working has not only been effective, but it has also shown a promising future due to the increased employee performance and satisfaction. However, remote work setting has also presented a few challenges that majorly depend on the type of leadership applied which also aligns with the needed work structure and culture.

The chapter would include an overview of the research design and rationale, study participants, sampling method and instrumentation, data collection, analysis, and ethical considerations taken in the design. Chapter 3 contains a descriptive discussion of the conduct of this study, and how it informed the problem. The detailed explanation supports future design replication, data collection, and analysis. The One-way Multivariate Analysis of Variance (MANOVA), and SPSS data analysis approach would allow valid and reliable data processing. The chapter’s discussion on limitations and delimitations expands the discussion in chapter one. The researcher intends to use SurveyMonkey collect participants from across the United States.

Based on the application of this design in establishing the connection between variables (independent and dependent) (Bloomfield, & Fisher, 2019), this quantitative casual comparative study was objectified to establish the significance of various leadership styles on employee performance, motivation, and job satisfaction in a remote setting. It was without a doubt that working remotely has been continuously adapted, particularly after the onset of the COVID-19 pandemic. In correlation to this, it was paramount to understand the aspects of remote working and what it entails in terms of productivity. As Bloomfield and Fisher (2019) establish, a quantitative casual comparative study supports the comparison of two variables. As such, the study’s selected design would facilitate the comparison of five essential levels of leadership styles commonly associated with working environments in relation to job satisfaction, motivation, and employee satisfaction.

With the aid of questionnaires, this study’s research questions would include: 1) Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles? 2) Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles? 3) Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles? These questions govern this study’s research. Given the nature of the study, the independent variable was defined by the five levels of leadership styles, including structural leadership, participative leadership, servant leadership, freedom-thinking leadership, and transformational leadership (Alheet, Adwan, Areiqat, Zamil, & Saleh, 2021). The dependent variables to be discussed in this section were performance, motivation, and satisfaction. With remote working being the mantra in most organizations globally, this study would make significant contributions towards revolutionizing and enhancing productivity in this type of setting. For applicable results, the sample size in this study was 60 remote workers. The estimated sample size used for this study was 45 remote workers. Fifteen percent would be added for possible attrition, and another 15% would be added for possible use of nonparametric tests. Thus 30% totaled would be added to the sample size of 45 to get a sample size of 60. The researcher has obtained the number of workers from G*Power (see Appendix G).

The purpose of this study was to determine which leadership style (Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leader) maximizes the dependent variables (Job Performance, Motivation, and Job Satisfaction) for remote workers. The instruments that would be used for the study would be the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). The number of participants was generated from G*Power (see Appendix G). The independent variable would be Leadership Styles. The dependent variables would be Job Performance, Motivation, and Job Satisfaction.

Table1.
Variables Table

The following research questions guide this quantitative study:

RQ1: Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

The population would be remote employees working remotely from across the USA. The target population would be remote employees who have 6 years of experience and were between the ages of 18 to 63. The unit of analysis was the individual employee.

The researcher would be recruiting participants using two recruitment plans. The first plan for recruitment would be using Survey Monkey and the alternative plan, in the event not enough participants can be obtained, would be to use Facebook groups.

When conducting quantitative research, the sample size calculation was based on the researcher’s needed effect size, which was the difference between the mean responses of the two groups, the alpha error or false positive error, and the statistical power (Gogtay, 2010). A priori analysis was conducted utilizing G*Power 3.1.9.4 (see Appendix G) software to determine the minimum necessary sample size for this study to achieve significance.

The estimated sample size used for this study was 45 remote workers. 15 percent would be added for possible attrition, and another 15% would be added for possible use of nonparametric tests. Thus 30% totaled would be added to the sample size of 45 to get a sample size of 60. The effect size would be .15, the alpha level would be .5, the power would be .8, the number of groups would be 5, and the number of response dependent variables would be 3. Each participant would be informed of the research objectives and fill out consent forms (see Appendix A) before participating in the study. Data collected would be kept confidential by the researcher for 3 years. After 3 years the data would be deleted, or shredded (Bloomfield & Fisher, 2019). There would also be an age range for the participants from 18 to 64 years of age.

Instrumentation refers to the tools or means researchers used to measure various research (Leung, 2001). Each instrument was selected based on the research goals. The research would use a questionnaire to collect information on various variables related to leadership styles in a remote setting (ie. work from home). According to (Leung, 2001), questionnaires were used to collect information from participants the researcher was interested in. A questionnaire was applicable in research when collecting factual data. Consequently, the investigators must ensure that the questionnaires were highly structured to allow the same types of information to be collected from many people in the same way and for data to be analyzed quantitatively and systematically (Leung, 2001). The research would use questionnaires to obtain critical information on independent variables. The instruments used for the study would be comprised of the demographic characteristic’s questions (see Appendix B), the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) also known as Job Satisfaction Index (JSI) (see Appendix I). For JSS, the Cronbach’s Alpha (α) coefficient of internal consistency was used to measure the reliability of the JSI constructed from the survey data. The tool was internally consistent if α was equal to or bigger than 0.7 (Leung, 2001). 4 dimensions had an α value greater than 0.65. However, internal consistency and reliability of the tool. indicate that the calculated JSI was reliable and internally consistent.

For the reliability of the individual items of MLQ, CFA regression weights for the MLQ-5X indicated that all the items presented a λ ≥ .50 (R 2 ≥ .25), ranging from .51 to .83. The exceptions were item four, concerning Active Management by Exception subscale, with a λ = .17 and, item 17, concerning Passive Management-by Exception subscale, with a λ = .20, showing a reduced contribution for the leadership constructs they represent. Regarding CFA regression weights for the MSLS, all the items presented a λ ≥ .50, ranging from .54 to .91. With no exception, all MSLS items showed a significant contribution for the constructs they represent. Regarding construct reliability, and the composite reliability (CR) criteria, the MSLS and MLQ-5X did not present problems in this domain, showing a good reliability of the leadership subscales (CR ≥ .70) (Table 2). Even though the Cronbach’s alpha criteria of two of the MLQ-5X subscales (Management-by-Exception Active and Management-by-Exception Passive) assumed problems of internal consistency, their values were near the acceptable (i.e., α =.687 and α = .696, for this study).

 For the IWPQ subscales, a mean score was calculated by adding the item scores and dividing their sum by the number of items in the subscale. Hence, the IWPQ yields three subscale scores that range between 0 and 4, with higher scores reflecting higher task and contextual performance, and higher counterproductive work behavior. The psychometric properties of the IWPQ have been tested and results indicated good to excellent internal consistency for task performance (α = 0.78), contextual performance (α = 0.85) and counterproductive work behavior (α = 0.79).

Information pertaining to the significance of different leadership styles (independent variable) as applied in a remote setting would be collected with the aid of questionnaires. The dependent variables for this study would include job satisfaction, motivation, and employee performance as tabulated above. Responses from the questionnaires would be used adequately for the collection of data. The validity and reliability of the instruments used for data collection was vital as they would shape the results of the study (Heale & Twycross, 2015). For this study, the general population was the Work from Home (WFH) employees.

Following approval from the South University Institutional Review Board (IRB), an electronic survey would be deployed via organizational email. To collect data for this study, the researcher would use a convenience sample of remote Work from Home (WFH) employees. In convenience sampling, “the ease with which potential participants can be located or recruited was the primary consideration” (Sarstedt et al., 2018). Participants had to be remote works from any state. The criteria to participate were /based upon descriptions of age range, and work from home experience. To determine the appropriate sample size, the researcher used G*Power 3.1.9.4 software.

Prior to participation individuals who were interested in completing the survey based upon the email received would be provided with the informed consent information (see Appendix A). The informed consent details the participants’ rights while participating in the study, including explaining how the collected data would be used. This informed consent also would document the overall purpose and intent of this study. Finally, the informed consent information would include any potential risks and benefits, also information about resources available if the participant was harmed in any way during the study (see Appendix A). Participants would be informed that there would be no compensation for their participation and that the participation in the research study would be completely voluntary. Those who desired to proceed with the survey would be asked an inclusionary question to determine if they meet the requirements for participation in the study. These individuals would be asked if they were at least the age of 18 through 63, if they were a remote worker, if they have at least 6 months of experience in remote work, and if they were male or female. If the individuals do not meet these requirements, they would be excluded from participation in this study.

Validity was described as the extent to which quantitative research measure or instrument accurately assesses what it was to measure (Heale & Twycross, 2015). In this sense, it ascertains that the results computed were applicable, and accurate. For this study, different types of validity would be considered, including external, internal, criterion-related, construct, and content validity. While external validity refers to the extent to which the research findings can be generalized to other populations or settings, internal validity refers to the extent to which the research can establish cause-and-effect relationships between variables (Heale & Twycross, 2015). Additionally, criterion-related validity refers to the relation between the research instrument and the external criteria. According to Heale and Twycross (2015), construct validity refers to the extent to which the research instrument measures the defined construct, and the content validity refers to the extent to which the research instrument or measure covers all aspects of the construct. With the consideration of the documented information, the role of validity in this research revolves around ensuring that the research measure and instrument was accurate and attain the desired objective in relation to assessing what was intended to be measured.

The survey instruments used for the study would be comprised of the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), and the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), and individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). Data for the survey would stem from G*Power (see Appendix G). The researcher was the only individual who could access the file as the computer was password protected. Data would be kept on the computer for three years after the study was completed (University of Virginia, 2022) The statistical software program (SPSS Version 27) would be used in the research once responses were gathered. The MLQ, IWPQ, and JSS has been found to be a psychometrically strong measure with a Cronbach’s alpha, indicating good internal consistency (O’Connor & Casey, 2015). Test-retest results for the scale indicate good reliability (r=0.797, p<0.001)’ (O’Connor & Casey, 2015). Additionally, scale developers found there to be adequate assessment in the areas of measurement error, content validity, hypotheses testing, and structural validity’ (O’Connor & Casey, 2015).

Reliability was significantly intertwined with how trustworthy the attained results were and its application in the study to eliminate possible errors and threats (Heale & Twycross, 2015). Heale and Twycross (2015) documented that reliability refers to “the extent to which a research instrument or measure produces consistent and stable results over time.” This study would consider inter-rater reliability which refers to the extent to which different persons produce consistent results as well as internal consistency reliability which refers to the extent to which the questions in the questionnaire was related to each other. The reliability of this study can be attained by testing the validity of the instruments used as well as taking measures to minimize the measurement error.

The reliability of the instruments being used was an essential part of the research study. The reliability concept deals with the assessments’ ability to be duplicated while the results were trustworthy across different settings (Rollnick et al., 2019). Threats to reliability exist throughout the entire process of research, and researchers need to be proactive and try to minimize these threats to the research as much as possible (McClelland et al., 2015). The instruments’ reliability refers to the level at which the collection tool being used can present stable and consistent results. The study’s reliability was always sample dependent, so it may vary from study to study (Scollione & Holdan, 2020). The level of reliability determines the overall accuracy of the results (Mohajan, 2017). When a study has high levels of reliability, another researcher should be able to replicate the study and reassess the outcomes (Rutkowski & Delandshere, 2016). This researcher considered several different types of reliability for this study, and each type was uniquely relevant to the situations where measurements were used (Kamper, 2019).

While descriptive statistics facilitate the completion of different variables of a study, inferential analysis supports the investigation of the relationship between dependent and independent variables. In correlation to this, it was without a doubt that these analysis tools were of significance to this study. With the aid of these tools, the data collected would be analyzed by integrating MANOVA (Multivariate Analysis of Variance), through which each of the variables would be analyzed at a given time (Scheiner, 2020). The use of a 5-Likert scale would play a critical role in the collection of data since it supports the assignment of numeric values to the leadership questions in the questionnaire. In this same context, the dependent variables would be measured on a 5-point Likert scale, with number 1 being termed as strongly disagree while number 5 would be assigned to strongly agree. The center of the scale would read “neither agree nor disagree”. Additionally, SPSS (Version 27) would be used in analyzing the data.

Technology would be instigated to facilitate the procedures of the research, particularly in selecting the sample population. Organizations that have adopted remote working would be contacted to provide access to their employees. A representative sample of 60 remote workers would be scheduled to answer the questionnaires. However, the participants would be required to have worked remotely for at least 6 months. Also, the willingness of the employees to take part in the study was significant as it would ensure accurate results would be collected. The questionnaires would be disseminated, answered, and submitted online, with strict adherence to a governing set of rules.

The research procedure for this study would entail sample selection, through which participants from remote working settings would be selected. The next procedure would be collecting data on the styles of leadership of different leaders, with the consideration of employee performance, motivation, and job satisfaction. Significant methods that would facilitate data collection include questionnaires and possibly performance evaluation. The most critical part of this study was defined by data analysis, which would make use of statistical methods, including MANOVA as discussed above. This step would provide insight on job satisfaction, motivation, and employee performance as related to various leadership styles.

The selected participants would be required to be willing to provide honest and unbiased information. They were also subjected to understanding what the study entails and what the data collected would be used for. The sample would be assured that their information would be protected and used only for the purpose of the study. This would be done by assuring them with a consent form that would be sent out online to each of the work-from-home employees. Taking the Belmont Report into account, the study ought to integrate the ethical principles of beneficence, respect for individuals involved, and non-maleficence (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979). Beneficence was to protect and defend the rights of others, prevent harm, remove conditions that would cause harm, help persons with disabilities, and rescue persons in danger (Varkey, B. (2021). Nonmaleficence (do no harm) Obligation not to inflict harm intentionally (Warren T. Jahn,2011). It was also critical for the interest of this study that the involved work from home employees remained anonymous to eradicate any form of possible opinion bias and scrutiny. In correlation to this, confidentiality and anonymity would be highly integrated throughout the study. Confidentiality was rules that promise someone sets through agreements that limit access, or places restrictions on different types of information (Warren T. Jahn,2011). Anonymity was when a person was identified as unknown, untraceable, or unreachable. The difference between the two was anonymity was not guaranteed if any personally identifiable (PII) information would be collected (Warren T. Jahn,2011). Confidentiality was when only the investigator(s) can identify the responses of individual participants (Varkey, B. (2021). 

This study adhered to the ethical guidelines for conducting quantitative research as documented by Belmont Research (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979), through which it was ensured that the participants were treated with upmost respect and beneficence was integrated as well. Additionally, the vital parameters of confidentiality, credibility, confirmability, and transferability were used to stipulate an enhanced research process. These parameters ensured that the study valued the relevance of moral principles and ethics. In addition to this, the ethical standards of this study played a central role in the data processing and associated procedures. Before using this, the researcher obtained permission to use it from the creators of the survey instrument. The researcher would explain why she used it and mention that the study’s data would be shared with the creators of the survey if requested. The researcher would not provide actual data results from the study but would only share the summary of the findings. All the data from the surveys, including the surveys themselves, was kept secure and stored on a flash drive and held in a locked cabinet. Any data collected by a paper form would be stored for three years, after which the data shall be destroyed appropriately, and digital data shall be deleted from the flash drive after three years as well (Redus, 2020).

Delimitations and Limitations

For starters, it was hoped that the participants would provide accurate results that would not contaminate the collected information. Despite involving the organizations that have adopted remote working, there was a risk that some participants contacted don’t have relative experience as remote workers. It was also notable that a significant population work remotely, and as such, could pose a threat to the results of the study. The only bias associated with this study was attributed to the limit of only using remote workers as the sample population of choice.

Quantitative research stems from positivism and assumes that there was an objective, rationally organized reality which was independent of researchers’ perceptions as well as those who participate in research (Needleman & Needleman, 1996; Slevitch, 2011).

The quantitative risk analysis allows you to determine the potential risk of a project. This can help you decide if a project is worth pursuing. It also was useful in the development of project management plans, as understanding the risks present allows you to reduce the likelihood of certain risks and to prepare for others that you cannot fully eliminate.

In many instances, poor research design or a lack of synergy between the different contributing variables in your systematic investigation can infuse bias into your research process. Research bias also happens when the personal experiences of the researcher influence the choice of the research question and methodology. In quantitative research, the researcher often tries to deny the existence of any bias, by eliminating any type of bias in the systematic investigation. Sampling bias was one of the most types of quantitative research biases and it was concerned with the samples you omit and/or include in your study. 

Once the data was cleaned, data screening would be conducted to assess the underlying assumptions. SPSS would be used to evaluate the assumptions of normality, homogeneity of variance-covariance matrices, linearity, and multicollinearity (Tabachnick & Fidell, 2013).

Descriptive and inferential statistics would be used to analyze data to determine if assumptions were met. The research questions address potential differences between multiples dependent variables; therefore, a one-way multivariate analysis of variance (MANOVA) would be utilized to analyze (French et al., 2008). Descriptive statistics were used to summarize the data and inferential statistics were used to test the hypotheses. MANOVA also allows for a more accurate and comprehensive picture of the phenomena being studied by the researcher (Allen, 2017). Finally, measuring the multiple response variables together would provide more chances at discovering the factor that was central to the investigation (Allen, 2017). A one-way MANOVA answered the five research questions regarding what leadership style characteristics their supervisors or managers fall under when measuring the dependent variables. The level of significance was p < .05, meaning there was a 5% chance that a difference existed in the 5 leadership styles. The Alpha level was the probability of rejecting the null hypothesis when the null hypothesis was true. Also, the current study would determine whether a mean difference exists between those five leadership styles as well. Conducting an F-test could provide an overall comparison of whether the means of the five groups of five leadership styles, and if the obtained F was larger than the critical F, the null hypotheses was rejected (Gravetter & Larry, 2016). A null Hypothesis was when there was no relationship between variables, and no differences between groups. The one-way MANOVA creates a linear combination of the three dependent variables to generate a grand mean and determine if there were group differences in the dependent variables.

In order to run a one-way MANOVA, ten assumptions needed to be addressed one at a time to ensure the sample could be analyzed using this test, which consisted of (1) two or more dependent variables on a continuous level, (2) one independent variable has two or more categorical, independent groups, (3) independence of observation, (4) no univariate or multivariate outliers, (5) multivariate normality, (6) no multicollinearity, (7) linear relationship between dependent variable for each independent group, (8) adequate sample size, (9) homogeneity of variance-covariance matrices, and (10) homogeneity of variances (Statistics, 2015).

Assumption 2 requires one independent variable with two or more categorical, independent levels (Statistics, 2015). The term level was typically reserved for groups that have an order (Statistics, 2015). The study has one independent variable (Leadership Styles) with five levels (structural leader, participative leader, servant leader, freedom-thinking, leader, and transformational leader). The second assumption was satisfied.

Assumption 3 requires independence of observation where there was no relationship between the participants in any of the groups. Having different participants in each group was a way to address this assumption (Statistics, 2015). Assumption three was met as the data had different participants in each of the multigroup.

This assumption was commonly tested in SPSS by following the Explore procedure. Any data that was more than 1.5 box-lengths from the edge of their box was classified by SPSS as outliers and was noted by circular icons, and data more than three box-lengths away was noted by an asterisk. For the second portion of the fourth assumption, the presence of multivariate outliers for the three dependent variables can be examined using Mahalanobis distance (Laerd, 2015). Although this was not a fool proof method, it was the more straightforward approach (Statistics, 2015).

Assumption 5 requires multivariate normality, which means normally distributed data for each of the groups in the independent variable was expected (Statistics, 2015). This assumption was commonly tested by utilizing the Shapiro-Wilks test for normality in SPSS by following the seven-step Explore procedure. If the Sig. values of the Shapiro-Wilks test was greater than 0.05, the data was normal. If it was below 0.05, the data significantly deviates from a normal distribution. The value that was acceptable was the p-value. A P-P plot was best when used to explore extremely peaked distributions, while a Q-Q plot was best used to explore the influence of tails of a distribution. This test was commonly utilized if the sample size was less than 45 participants.

Assumption 6 requires that there be no multicollinearity, which means that the dependent variables should be reasonably correlated with each other (Statistics, 2015). If the correlations were too high (greater than 0.9), there was risk for multicollinearity, which was problematic for a MANOVA (Statistics, 2015). The threshold for determining whether multicollinearity was present was
r > .70 (Crossley, Subtirelu, & Salsbury, 2013). Utilizing the Bivariate procedure in SPSS, Pearson correlations between the dependent variables were analyzed to determine correlation between the variables (Statistics, 2015). If this assumption was violated, the researcher would consider using more conservative statistics for determining significance. The researcher can use alternative 
F statistics (Welch’s or Brown-Forsythe; see Field, 2013) to determine if the researcher has statistical significance.

Assumption 7 requires a linear association between the dependent variables for each group of independent variables (Statistics, 2015). A scatterplot matrix for each group of the independent variables identifies if there was linear relationship (a straight line) or not (a curved line). If the variables were not linearly related, then there was a loss of ability to identify differences (Statistics, 2015). In SPSS, after splitting the data file to separate out the independent levels, the Chart Builder procedure was utilized to assess linearity through scatterplot (Statistics, 2015).

Sample size. Laerd (2018) stated that the larger the sample size the better, but at a minimum, there need to be as many participants in each group of the independent variable as there was number of Assumption 8 requires a sufficient dependent variable. Assumption eight, demonstrating adequate sample size, was satisfied upfront by using a priori power analysis (see Appendix G). According to Cohen (1988), research studies with a medium (.50) to large (.80) effect size should contain 30 participants per cell (group) which should produce approximately 80% power.

Assumption 9 requires homogeneity (similar or comparable) of variance-covariance matrices (matrix of all possible pairs of variables) (Statistics, 2015). After un-splitting the file, the assumption could be tested by utilizing Box’s M test of equality of covariance in SPSS. The important row was the significance level (p-value) of the Box’s M test. If the test was not statistically significant (i.e., p > .001), there was homogeneity of variance-covariance matrices and no assumptions was violated (Statistics, 2015).

Assumption 10 requires homogeneity (same) of variances. Assuming the assumption of homogeneity of variance-covariance matrices were not violated, a Levene’s test of equality of variances procedure in SPSS was run (Statistics, 2015). The one-way MANOVA assumes that there were equal variances between the groups of the independent variable. The important column was the Sig. which represents the significance level (p-value) of the test. If the test was not statistically significant (greater than .05), there were equal variances and the assumption of homogeneity of variances has not been violated (Statistics, 2015).

Meeting assumptions was a requirement for obtaining accurate results when using a one-way MANOVA as seen below in Table 2; however, it was common for data to violate one or more of these assumptions. When data violate assumptions, the researcher must use correct data, use an alternative test, or proceed with the analysis despite the violation of assumptions.

The relevance of this study was associated with its contributions towards facilitating an understanding of the different leadership styles and the variables of job satisfaction, motivation, and employee performance in a remote setting. The study would explore the impact of structural, servant, freedom-thinking, participative, and transformational leadership styles on the productivity, performance, and satisfaction of employees. Taking the attained results into account, the study would provide evidenced results establishing the most productive leadership style. Additionally, the study would facilitate the development of strong bonds between employees and their leaders with the aim of enhancing employee performance, motivation, and job satisfaction.

Limitations refer to factors that may affect the generalizability or external validity of the study whereas delimitations refer to the specific choices made by the researcher in the design of the study (Theofanidis & Fountouki, 2018). While the selected research design would facilitate the attainment of applicable results, it was associated with various limitations and delimitations that would be discussed in this section. In consideration of the sample population, the length of remote working experience was a limitation of interest. Without sufficient experience, the study could yield undesirable results. Another limitation was tied down to the problem statement in the sense that only remote workers were considered. It would be of importance if traditional workers would participate in the study as it would facilitate a usable comparison of the various leadership styles utilized.

Provide a brief restatement of the research question and hypotheses.

1. Was there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

Hypothesis 1 Null: There was not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Hypothesis 1 Alternant: There was a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

2. Was there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

Hypothesis 2 Null: There was not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Hypothesis 2 Alternant: There was a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

3. Was there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

Hypothesis 3 Null: There was not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Hypothesis 3 Alternant: There was a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

The researcher prepared a close-ended questionnaire using SurveyMonkey to collect data and responses. Among the four key research questions, the researcher created three individual questions around each research question. The respondents were expected to answer whether they agree or disagree with experiencing a leadership trait and explain their job performance during that month. I summed up the responses that agreed with their productivity and the reactions ` disagreed with their productivity to get a value that I would use as a response for the main research question.

The questionnaire tested the respondents on a motivation factor and its influence on motivation and productivity. The responses that indicated the participant did not experience a motivation factor (clients who answers ‘no’ to the questions) were not considered in the study since we were interested only in the presence of the motivation factor (clients who answers ‘yes’ to the questions).

To calculate the correlation coefficient in excel, I used the
Correlation function.

Research Question 1

Identify the alternative hypothesis.

The null hypothesis: There was no impact of transformational management on employee motivation and job performance.

The alternative hypothesis: There was impacts of transformational management on employee motivation and job performance.

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

Figure 1:

After conducting a correlation analysis in excel, the following details were found.

If the test concludes that the correlation coefficient was significantly different from zero, we say that correlation coefficient is significant. With that being said, we accept the alternative hypothesis that there were impacts of transformational management on employee motivation and job performance.

Research Question 2

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

Figure 2:

After conducting a correlation analysis in excel, the following details were found.

The Pearson’s correlation value was 0.39, which was not 0, and we accept the alternative hypothesis that Rewards, and punishment affect employees’ performance, motivation, and job satisfaction.

Research Question 3

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

Figure 3:

After conducting a correlation analysis in excel, the following details were found.

The Pearson’s correlation value was 0.48, which was not 0, and we accept the alternative hypothesis that Delegation motivates employees and leads to job satisfaction and better performance.

Research Question 4

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

Figure 4:

After conducting a correlation analysis in excel, the following details were found.

The Pearson’s correlation value was 0.90, which was not 0, and thus we accept the alternative hypothesis that there were impacts of visionary leaders on motivation, employee performance, and job satisfaction.

While descriptive statistics facilitate the completion of different variables of a study, inferential analysis supports the investigation of the relationship between dependent and independent variables. In correlation to this, it will be without a doubt that these analysis tools were of significance to this study. With the aid of these tools, the data collected would be analyzed by integrating MANOVA (Multivariate Analysis of Variance), through which each of the variables would be analyzed at a given time (Scheiner, 2020). The use of a 5-Likert scale would play a critical role in the collection of data since it supports the assignment of numeric values to the leadership questions in the questionnaire. In this same context, the dependent variables would be measured on a 5-point Likert scale, with number 1 being termed as strongly disagree while number 5 would be assigned to strongly agree. The center of the scale would read “neither agree nor disagree”. Additionally, SPSS (Version 27) would be used in analyzing the data.

Technology would be instigated to facilitate the procedures of the research, particularly in selecting the sample population. Organizations that have adopted remote working would be contacted to provide access to their employees. A representative sample of 60 remote workers would be scheduled to answer the questionnaires. However, the participants would be required to have worked remotely for at least 6 months. Also, the willingness of the employees to take part in the study was significant as it would ensure accurate results would be collected. The questionnaires would be disseminated, answered, and submitted online, with strict adherence to a governing set of rules.

The research procedure for this study would entail sample selection, through which participants from remote working settings would be selected. The next procedure would be collecting data on the styles of leadership of different leaders, with the consideration of employee performance, motivation, and job satisfaction. Significant methods that would facilitate data collection include questionnaires and possibly performance evaluation. The most critical part of this study was defined by data analysis, which would make use of statistical methods, including MANOVA as discussed above. This step would provide insight on job satisfaction, motivation, and employee performance as related to various leadership styles.

Validity was described as the extent to which quantitative research measure or instrument accurately assesses what it was to measure (Heale & Twycross, 2015). In this sense, it ascertains that the results computed were applicable, and accurate. For this study, different types of validity would be considered, including external, internal, criterion-related, construct, and content validity. While external validity refers to the extent to which the research findings can be generalized to other populations or settings, internal validity refers to the extent to which the research can establish cause-and-effect relationships between variables (Heale & Twycross, 2015). Additionally, criterion-related validity refers to the relation between the research instrument and the external criteria. According to Heale and Twycross (2015), construct validity refers to the extent to which the research instrument measures the defined construct, and the content validity refers to the extent to which the research instrument or measure covers all aspects of the construct. With the consideration of the documented information, the role of validity in this research revolves around ensuring that the research measure and instrument was accurate and attain the desired objective in relation to assessing what was intended to be measured.

The survey instruments used for the study would be comprised of the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), and the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), and individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). Data for the survey would stem from G*Power (see Appendix G). The researcher was the only individual who could access the file as the computer was password protected. Data would be kept on the computer for three years after the study was completed (University of Virginia, 2022) The statistical software program (SPSS Version 27) would be used in the research once responses were gathered. The MLQ, IWPQ, and JSS has been found to be a psychometrically strong measure with a Cronbach’s alpha, indicating good internal consistency (O’Connor & Casey, 2015). Test-retest results for the scale indicate good reliability (r=0.797, p<0.001)’ (O’Connor & Casey, 2015). Additionally, scale developers found there to be adequate assessment in the areas of measurement error, content validity, hypotheses testing, and structural validity’ (O’Connor & Casey, 2015).

Reliability was significantly intertwined with how trustworthy the attained results were and its application in the study to eliminate possible errors and threats (Heale & Twycross, 2015). Heale and Twycross (2015) documented that reliability refers to “the extent to which a research instrument or measure produces consistent and stable results over time.” This study would consider inter-rater reliability which refers to the extent to which different persons produce consistent results as well as internal consistency reliability which refers to the extent to which the questions in the questionnaire was related to each other. The reliability of this study can be attained by testing the validity of the instruments used as well as taking measures to minimize the measurement error.

The reliability of the instruments being used was an essential part of the research study. The reliability concept deals with the assessments’ ability to be duplicated while the results were trustworthy across different settings (Rollnick et al., 2019). Threats to reliability exist throughout the entire process of research, and researchers need to be proactive and try to minimize these threats to the research as much as possible (McClelland et al., 2015). The instruments’ reliability refers to the level at which the collection tool being used can present stable and consistent results. The study’s reliability was always sample dependent, so it may vary from study to study (Scollione & Holdan, 2020). The level of reliability determines the overall accuracy of the results (Mohajan, 2017). When a study has high levels of reliability, another researcher should be able to replicate the study and reassess the outcomes (Rutkowski & Delandshere, 2016). This researcher considered several different types of reliability for this study, and each type was uniquely relevant to the situations where measurements were used (Kamper, 2019).

Table 1:

Reliability Statistics

Mean and standard deviation values were presented in Table 1. Descriptive statistics summarize a given dataset, which can either be presented in the form of a sample or population. Here, descriptive statistics were measured through measures of speed or variation and central tendency, including mean values. The mean score for Freedom-Thinking Leadership were (M = 2.77, of which SD = 1.74) did not significantly differ from Servant Leadership (M = .642, SD = 6.378), and Participative (Democratic) Leadership were (M = 2.481, SD = .7989). However, turnover’s mean was significantly different than Structural Leadership (M = 2.742, SD = .6690).

Table 2:

Descriptive Statistics

Figure 1.

Histogram of mean for
Structural Leadership, Participative (Democratic) Leadership, Servant Leadership, Freedom-Thinking Leadership, and Transformational Leadership with Pareto line.

Screening was conducted to assess the underlying assumptions. SPSS was used to evaluate the assumptions of normality, homogeneity of variance-covariance matrices, linearity, and multicollinearity.

The first assumption requires two or more dependent variables measured at the continuous level (Statistics, 2015). Assumption one was satisfied for the study, as there were three dependent variables measured on a Likert-type scale, which was commonly accepted to be continuous in the field of the social sciences.

The second assumption requires one independent variable with two or more categorical, independent levels (Statistics, 2015). The term level was typically reserved for groups that have an order (Statistics, 2015). The study has one independent variable (Leadership Styles) with five levels (structural leader, participative leader, servant leader, freedom-thinking, leader, and transformational leader). The second assumption was satisfied.

The third assumption requires independence of observation where there was no relationship between the participants in any of the groups. Having different participants in each group was a way to address this assumption (Statistics, 2015). Assumption three was met as the data had different participants in each of the three groups.

The fourth assumption requires no univariate or multivariate outliers (Statistics, 2015). This assumption was commonly tested in SPSS by following the Explore AI procedures then visually analyzing box plots to detect outliers. Any data that was more than 1.5 box-lengths from the edge of their box was classified by SPSS as outliers and was noted by circular icons, and data more than three box-lengths away was noted by an asterisk. Although this was not a foolproof method, it was the more straightforward approach (Statistics, 2015). The researcher made the decision to remove the outlier that exceeded the Mahalanobis distance critical alpha value since the one-way MANOVA was robust to multivariate outlier (Laerd, 2015).

Figure 2:

Multivariate Boxplot

The fifth assumption requires multivariate normality, which means normally distributed data for each of the groups in the independent variable was expected (Statistics, 2015). This assumption was commonly tested by utilizing the Shapiro-Wilks test for normality in SPSS by following the seven-step Explore procedure. This test was commonly utilized if the sample size was less than 60 participants. There were as many Shapiro-Wilks tests as there were groups of independent variables multiplied by the number of dependent variables.

Table 4:

Shapiro-Wilk Test of Normality

As for the looks of the Shapiro -Wilks Chart, it looks like it was indeed significant. This means that the distribution data was not normal, which would make it non-normality. To deal with this it would be best to use the non-parametric test, which does not assume normality or transform the data using an appropriate function. This would force it to fit normal distribution.

The sixth assumption requires that there be no multicollinearity, which means that the dependent variables should be reasonably correlated with each other (Statistics, 2015). If the correlations were too high (greater than 0.9), there was risk for multicollinearity, which was problematic for a MANOVA (Statistics, 2015). Utilizing the Bivariate procedure in SPSS, Pearson correlations between the dependent variables were analyzed to determine correlation between the variables (Statistics, 2015). The threshold for determining whether multicollinearity was present was
r > .70 (Crossley, Subtirelu, & Salsbury, 2013). The five independent variables,
r (48) = .51,
p < .001) in the MANOVA correlations were below the threshold of .70 (Crossley et al., 2013), the sixth assumption was satisfied.

The seventh assumption requires a linear association between the dependent variables for each group of independent variables (Statistics, 2015). A scatterplot matrix for each group of the independent variables identifies if there was linear relationship (a straight line) or not (a curved line). If the variables were not linearly related, then there was a loss of ability to identify differences (Statistics, 2015). In SPSS, after splitting the data file to separate out the independent levels, the Chart Builder procedure was utilized to assess linearity through scatterplot (Statistics, 2015).

Figure 3:

Scatter Plot

The eighth assumption requires a sufficient sample size. Laerd (2018) stated that the larger the sample size the better, but at a minimum, there need to be as many participants in each group of the independent variable as there was number of dependent variables. Assumption eight, demonstrating adequate sample size, was satisfied upfront by using a priori power analysis. When conducting quantitative research, the sample size calculation was based on the researcher’s needed effect size, which was the difference between the mean responses of the two groups, the alpha error or false positive error, and the statistical power (Gogtay, 2010). A priori analysis was conducted utilizing G*Power 3.1.9.4 (see Appendix G) software to determine the minimum necessary sample size for this study to achieve significance.

The estimated sample size used for this study was 60 remote workers. Fifteen percent would be added for possible attrition, and another 15% would be added for possible use of nonparametric tests. Thus 30% totaled would be added to the sample size of 45 to get a sample size of 60. The effect size would be .15, the alpha level would be .5, the power would be .8, the number of groups would be 5, and the number of response dependent variables would be 3.

The ninth assumption requires homogeneity (similar or comparable) of variance-covariance matrices (matrix of all possible pairs of variables) (Statistics, 2015). The assumption could be tested by utilizing Box’s M test of equality of covariance in SPSS (see table 4). The important row was the significance level (p-value) of the Box’s M test. If the test was not statistically significant (i.e., p > .001), there was homogeneity of variance-covariance matrices and no assumptions would not be violated (Statistics, 2015). Box’s
M = 9.342,
p < .001. Therefore, the assumption was violated (See Table 4). The one-way MANOVA was still conducted because it was robust to violations with sample size; however, this may distort the alpha level (Warner, 2008). Box’s M test is significant, Pillai’s trace criterion should be used to interpret the multivariate test results, because it will be more robust to departures from assumptions.

Table 4:

Box’s Test of Equality of Covariance Matrices

The tenth assumption requires homogeneity (same) of variances. Levene’s test assesses this assumption. It tests the null hypothesis that the population variances are equal (called homogeneity of variance) (Laerd Statistics, 2015). Assuming the assumption of homogeneity of variance-covariance matrices were not violated, a Levene’s test of equality of variances procedure in SPSS was ran (Statistics, 2015). The one-way MANOVA assumes that there were equal variances between the groups of the independent variable. The important column was the Sig. which represents the significance level (p-value) of the test. If the test was not statistically significant (greater than .05), there were equal variances and the assumption of homogeneity of variances has not been violated (Statistics, 2015). If the test was not statistically significant (
p > .05), there was similar variances and the expectation of homogeneity of variances has not been broken (Laerd Statistics, 2015). For Structural leadership, the significance was less than .05 (
p = .015), for the Participative leadership, the significance was less than .05 (
p = .005), for Servant leadership, the significance was less than .05 (
p = .009), for Freedom-thinking leadership, the significance was less than .05 (
p = .001), and for Transformational leadership, the significance was also less than .05 (
p = .091). For the variables of interest as indicated in Table 5, which means that this assumption was violated.

Table 5:

Levene’s Test: Independent Sample Test

Table 6:

Assumption Strategies for One-Way MANOVA

The following research questions guide this quantitative study:

RQ1: Was there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There was not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There was a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Was there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There was not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There was a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Was there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There was not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There was a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Chapter 4 presented descriptive statistics of the collected data, reviewed the data analysis procedures, and presented the results of the data analysis for the study. The purpose of this quantitative, causal-comparative research study was to determine the relationship between independent and dependent variables by establishing role of leadership styles on employee performance, motivation, and job satisfaction in a remote setting. With the aid of questionnaires, significant information would be collected from a sample size of 60 remote workers. A quantitative methodology would be integrated to scrutinize and analyze the data collected, forming the basis of this third chapter. In correlation with the limitation attributed to the sample population, various challenges were associated with the study. Regardless, the ethical standards in association facilitated the attainment of dependable results. With an understanding of the methodology to be incorporated, the subsequent chapter will cover the vital aspects of data collection and analysis.

There were three research questions for this study and the corresponding hypotheses that were addressed included:

RQ1: Was there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There was not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There was a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Was there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There was not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There was a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Was there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There was not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There was a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Limitations. The analysis addressed if there was a relationship between the dependent and independent variables, but it did not offer a reason why a relationship was present. Potential limitations were also laid in self-reporting. Ostroff and Kozlowski (1992) noted although self-reports in data was appropriate when interest was in newcomer perceptions, a comparison of newcomers’ perceptions to those of other sources of information may have been fruitful for further understanding the process. When stating newcomer, this means a person that has recently arrived in a place or joined a group (Theofanidis & Fountouki, 2018).

During the data analysis process, there were two identified limitations: (1) assumptions not met and (2) outliers. It was essential to discuss the limitations that emerged during the data analysis and how these limitations may affect the interpretation of the results. The first limitation were assumptions that were not met for the one-way MANOVA, and two-tailed Independent Samples t-test. All three tests had assumption violations in multivariate normality, no univariate or multivariate outliers, homogeneity of variance-covariance matrices, and homogeneity of variances.

Chapter 4 described the data collected including descriptive statistics and data specific to each research question. After the data were analyzed, both the first and second research questions resulted in statistically significant results. Chapter 5 provided a summary of the study, findings, implications, and recommendations for future research.

The study’s findings illuminate the complex interplay between leadership styles and outcomes in remote work contexts. Positive correlations between distinct leadership traits and job performance, motivation, and satisfaction underscore effective leadership’s pivotal role in remote work environments. These results highlight the importance of tailoring leadership approaches to suit the unique dynamics of remote work settings, emphasizing the need for leaders to cultivate traits that foster productivity, motivation, and satisfaction among remote employees. Such insights can inform organizational strategies to optimize leadership practices and enhance remote workforce performance.

When dealing with workers in a remote work setting, the style of leadership approach must capture various essential details that affect employee motivation, performance, and job satisfaction. Any employee working in a remote work setting will be expected to experience several challenges which can vary depending on the individual’s personality and background. In most cases, employees will be expected to feel isolated, pressured, lack structure, and have difficulty in separating personal life and work (Chen, Liu, & Zhang, 2020). On the same note, workers in remote work setting have been described to have a lot of difficulties when it came to effective communication and collaborations among the management structure (Chen, Liu, & Zhang, 2020). These issues will be a direct result of geographical difference and the aspect of facing various additional problems. In response to these challenges, most employees in remote workplace tend to feel unmotivated and unsatisfied with their work since everyone tends to lose interest in that common goal. In any work setting, attaining effective leadership can be quite challenging which makes it even more difficult when it comes to remote setting. It’s important to start by noting that leadership plays a very crucial role in promoting effective communication that translates to a proactive and productive workforce (Chen, Liu, & Zhang, 2020). Following the above comment, remote workplace creates a bit complicated scenario of which the absence of physical leadership prompts out various challenges such lack of motivation, guidance, and most importantly support from one another. Leadership style has a significant connection with how employees view their work experience and how they find their place within an organization (Karim & Abbas, (2020). For instance, Participative and Transformational Leadership Styles have been commended on improving employee performance while at the same time increasing their job satisfaction (Karim & Abbas, (2020).

Over the past few decades, there has been a lot of research done into styles of leadership and the context in which they suitably work. In general, leadership style can be described as an approach or structure used to direct or coordinate team or teams to achieve a common goal. Therefore, it’s essential to note that leadership plays a crucial role in any organization in relation to providing employees with motivation, direction, and purpose of achieving the organization’s mission and goals. According to Araz & Mehr (2021), for any leadership style to be considered effective, it must have a well-structured communication channel that allows smooth flow of information without or with minimal distortion. An effective leadership style must be in terms of delivering messages while at the same time positively influencing employee’s attitude (Araz & Mehr, 2021). These aspects have been identified in the paper as one of the main features that must be considered (when choosing a leadership style for remote workers).

Across this dissertation, there will be various styles covered which all have different approaches and applications. However, regarding the topic at hand, participative and transformational leadership style have a significant impact on remote working employees. These two types of leadership styles have been described to have a great influence on employee’s performance, motivation, and satisfaction. The main element that has contributed to this success will be the fact that employees will be able to express their ideas and emotions to one another by participating in decision making process (Allred et. Al., 2018). On the other hand, depending on the nature of work, the style of leadership also tends to vary with some work, such in the security sector, being sensitive than others thereby requiring more rigid structures.

To best address the problem statement and identified gap in the literature, three research questions and six hypotheses were developed to guide this study:

RQ1: Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

This study focused on the role of leadership styles in employee performance, motivation, and job satisfaction in a remote setting. The study will be conducted using instruments to collect data from remote employees. The study will be focused on employees across the United States.

Descriptive and inferential statistics will be used to analyze data. The research questions address potential differences between multiples dependent variables; therefore, a one-way multivariate analysis of variance (MANOVA) will be utilized to analyze (French et al., 2008). Descriptive statistics will be used to summarize the data and inferential statistics will be used to test the hypotheses. MANOVA also allows for a more accurate and comprehensive picture of the phenomena being studied by the researcher (Allen, 2017). Finally, measuring the multiple response variables together will provide more chances at discovering the factor that will be central to the investigation (Allen, 2017). A one-way MANOVA answered the three research questions regarding what leadership style characteristics their supervisors or managers fall under when measuring the dependent variables. The level of significance was p < .05, meaning there was a 5% chance that a difference existed in the 5 leadership styles. The Alpha level will be the probability of rejecting the null hypothesis when the null hypothesis is going to be true. Also, the current study will determine whether a mean difference exists between those five leadership styles as well. Conducting an F-test could provide an overall comparison of whether the means of the five groups of five leadership styles If the obtained F is going to be larger than the critical F, the null hypotheses will be rejected (Gravetter & Larry, 2016). A null Hypothesis will be when there will be no relationship between variables, and no differences between groups. The one-way MANOVA creates a linear combination of the three dependent variables to generate a grand mean and determine if there were group differences in the dependent variables.

Descriptive statistics summarize a given dataset, which can either be presented in the form of a sample or population. Here, descriptive statistics will be measured through measures of speed or variation and central tendency, including mean values. The mean score for Freedom-Thinking Leadership (M = 2.77, SD = 1.74) does not significantly differ from Servant Leadership (M = .642, SD = 6.378), Participative (Democratic) Leadership (M = 2.481, SD = .7989). However, turnover’s mean was significantly different than Structural Leadership (M = 2.742, SD = .6690).

The purpose of this study will be to determine which leadership style (Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leader) maximizes the dependent variables (Job Performance, Motivation, and Job Satisfaction) for remote workers. The instruments that would be used for the study would be the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). The number of participants was generated from G*Power (see Appendix G). The independent variable would be Leadership Styles. The dependent variables would be Job performance, motivation, and Job satisfaction.

When conducting quantitative research, the sample size calculation will be based on the researcher’s needed effect size, which will be the difference between the mean responses of the two groups, the alpha error or false positive error, and the statistical power (Gogtay, 2010). A priori analysis was conducted utilizing G*Power 3.1.9.4 (see Appendix G) software to determine the minimum necessary sample size for this study to achieve significance.

The estimated sample size used for this study was 45 remote workers. 15 percent would be added for possible attrition, and another 15% would be added for possible use of nonparametric tests. Thus 30% totaled would be added to the sample size of 45 to get a sample size of 60. The effect size would be .15, the alpha level would be .5, the power would be .8, the number of groups would be 5, and the number of response dependent variables would be 3. Each participant would be informed of the research objectives and fill out consent forms (see Appendix A) before participating in the study. Data collected would be kept confidential by the researcher for 3 years. After 3 years the data would be deleted, (Bloomfield & Fisher, 2019). There would also be an age range for the participants from 18 to 64 years of age.

The research would use questionnaires to obtain critical information on independent variables. The instruments used for the study would be comprised of the demographic characteristic’s questions (see Appendix B), the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) also known as Job Satisfaction Index (JSI) (see Appendix I). The tool is going to internally consistent if α will be equal to or bigger than 0.7 (Leung, 2001). Four dimensions had an α value greater than 0.65.

The purpose of this quantitative, causal-comparative research study was to determine which leadership style (Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leader) maximizes the dependent variables (Job Performance, Motivation, and Job Satisfaction) for remote workers. The instruments that will be used for the study will be the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). The number of participants was generated from G*Power (see Appendix G). The independent variable will be Leadership Styles. The dependent variables will be Job performance, motivation, and Job satisfaction.

The exceptions were item four, concerning Active Management by Exception subscale, with a λ = .17 and, item 17, concerning Passive Management-by Exception subscale, with a λ = .20, showing a reduced contribution for the leadership constructs they represent. Regarding CFA regression weights for the MSLS, all the items presented a λ ≥ .50, ranging from .54 to .91. With no exception, all MSLS items showed a significant contribution for the constructs they represent. Even though the Cronbach’s alpha criteria of two of the MLQ-5X subscales (Management-by-Exception Active and Management-by-Exception Passive) assumed problems of internal consistency, their values were near the acceptable (i.e., α =.687 and α = .696, for this study).

 For the IWPQ subscales, a mean score was going to be calculated by adding the item scores and dividing their sum by the number of items in the subscale. Hence, the IWPQ yields three subscale scores that range between 0 and 4, with higher scores reflecting higher task and contextual performance, and higher counterproductive work behavior. The psychometric properties of the IWPQ have been tested and results indicated good to excellent internal consistency for task performance (α = 0.78), contextual performance (α = 0.85) and counterproductive work behavior (α = 0.79).

With the aid of questionnaires, this study’s research questions will include: 1) Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles? 2) Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles? 3) Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles? These questions govern this study’s research. Given the nature of the study, the independent variable is defined by the five levels of leadership styles, including structural leadership, participative leadership, servant leadership, freedom-thinking leadership, and transformational leadership (Alheet, Adwan, Areiqat, Zamil, & Saleh, 2021). The dependent variables to be discussed in this section will be performance, motivation, and satisfaction. With remote working being the mantra in most organizations globally, this study will make significant contributions towards revolutionizing and enhancing productivity in this type of setting. For applicable results, the sample size in this study was 60 remote workers. The estimated sample size used for this study was 45 remote workers. Fifteen percent will be added for possible attrition, and another 15% will be added for possible use of nonparametric tests. Thus 30% totaled will be added to the sample size of 45 to get a sample size of 60. The researcher has obtained the number of workers from G*Power (see Appendix G).

Findings explained. To examine the Descriptive statistics will be used to summarize the data and inferential statistics will be used to test the hypotheses. MANOVA also allows for a more accurate and comprehensive picture of the phenomena being studied by the researcher (Allen, 2017). Finally, measuring the multiple response variables together will provide more chances at discovering the factor that will be central to the investigation (Allen, 2017). A one-way MANOVA =answered the three research questions regarding what leadership style characteristics their supervisors or managers fall under when measuring the dependent variables. The level of significance was p < .05, meaning there was a 5% chance that a difference existed in the 5 leadership styles. The Alpha level will be the probability of rejecting the null hypothesis when the null hypothesis is going to be true. Also, the current study will determine whether a mean difference exists between those five leadership styles as well. Conducting an F-test could provide an overall comparison of whether the means of the five groups of five leadership styles If the obtained F is going to be larger than the critical F, the null hypotheses will be rejected (Gravetter & Larry, 2016). A null Hypothesis will be when there is no relationship between variables, and no differences between groups. The one-way MANOVA creates a linear combination of the three dependent variables to generate a grand mean and determine if there were group differences in the dependent variables.

While descriptive statistics facilitate the completion of different variables of a study, inferential analysis supports the investigation of the relationship between dependent and independent variables. In correlation to this, it is going to be without a doubt that these analysis tools will be of significance to this study. With the aid of these tools, the data collected will be analyzed by integrating MANOVA (Multivariate Analysis of Variance), through which each of the variables will be analyzed at a given time (Scheiner, 2020). The use of a 5-Likert scale will play a critical role in the collection of data since it supports the assignment of numeric values to the leadership questions in the questionnaire. In this same context, the dependent variables will be measured on a 5-point Likert scale, with number 1 being termed as strongly disagree while number 5 will be assigned to strongly agree. The center of the scale will read “neither agree nor disagree”. Additionally, SPSS (Version 27) will be used in analyzing the data.

Technology will be instigated to facilitate the procedures of the research, particularly in selecting the sample population. Organizations that have adopted remote working will be contacted to provide access to their employees. A representative sample of 60 remote workers will be scheduled to answer the questionnaires. However, the participants will be required to have worked remotely for at least 6 months. Also, the willingness of the employees to take part in the study was significant as it would ensure accurate results will be collected. The questionnaires will be disseminated, answered, and submitted online, with strict adherence to a governing set of rules.

The research procedure for this study will entail sample selection, through which participants from remote working settings will be selected. The next procedure will be collecting data on the styles of leadership of different leaders, with the consideration of employee performance, motivation, and job satisfaction. Significant methods that will facilitate data collection include questionnaires and possibly performance evaluation. The most critical part of this study will be defined by data analysis, which will make use of statistical methods, including MANOVA as discussed above. This step will provide insight on job satisfaction, motivation, and employee performance as related to various leadership styles.

The survey instruments used for the study will be comprised of the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), and the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), and individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). Data for the survey will stem from G*Power (see Appendix G). The researcher will be the only individual who can access the file as the computer will be password protected. Data will be kept on the computer for three years after the study will be completed (University of Virginia, 2022). The statistical software program (SPSS Version 27) will be used in the research once responses is going to be gathered. The MLQ, IWPQ, and JSS has been found to be a psychometrically strong measure with a Cronbach’s alpha, indicating good internal consistency (O’Connor & Casey, 2015). Additionally, scale developers found there to be adequate assessment in the areas of measurement error, content validity, hypotheses testing, and structural validity’ (O’Connor & Casey, 2015).

Discussion of conclusions relative to findings and summary.

Theoretical implications. The findings from this study carry significant implications for leadership theory. They emphasize the necessity of transcending conventional classifications of leadership styles and prioritizing individual leadership traits instead. This recognition that leadership effectiveness isn’t restricted to one style but can be a blend of various traits enriches contemporary comprehension of leadership dynamics in work settings. The notion of hybrid leadership, which amalgamates complementary traits from diverse styles, emerges as a promising direction for future exploration and implementation in leadership theory (Chou, Liao, & Chen, 2021). By embracing this perspective, researchers and practitioners can better grasp the intricacies of effective leadership and develop more adaptable approaches that accommodate the multifaceted demands of modern organizational contexts.

Practical implications. The findings of this study offer valuable insights for organizational leaders and managers. By acknowledging the diversity of leadership traits and their impact on employee outcomes, organizations can adopt a more flexible and adaptive approach to leadership development. Managers should be encouraged to cultivate a diverse skill set encompassing various leadership traits, enabling them to tailor their leadership approach to remote work settings’ specific needs and challenges.

Furthermore, organizations can benefit from training and support to help managers navigate remote leadership’s complexities, including fostering team cohesion, promoting communication, and maintaining motivation and engagement among remote team members (Chen, Liu, Zhang, 2020). Investing in leadership development programs that enhance these skills can empower managers to lead their remote teams more effectively, resulting in improved performance, job satisfaction, and overall organizational success in remote work environments.

By embracing the insights from this study, and incorporating them into their leadership practices, organizations can create a conducive environment for remote work success, driving employee engagement, productivity, and well-being in the increasingly prevalent remote work landscape.

Future implications. Expanding on the findings, future research could delve into several avenues to deepen our understanding of leadership effectiveness in remote work settings. Longitudinal studies could provide valuable insights into the lasting impacts of different leadership styles and traits on employee outcomes, offering a more nuanced understanding of causal relationships.

Additionally, mixed methods approach integrating qualitative data collection methods such as interviews or observations could offer deeper insights into remote workers’ lived experiences and the influence of leadership on their work attitudes and behaviors. Furthermore, comparative studies examining leadership effectiveness across different work settings, such as remote versus co-located environments, could provide valuable insights into the unique challenges and opportunities associated with remote leadership (van der Velden, Kramer, & de Lange, 2020). By exploring these avenues, researchers can contribute to a more comprehensive understanding of effective leadership in remote work settings, informing the development of tailored strategies to optimize leadership practices and enhance remote workforce performance.

The positive correlations observed between transformational leadership and job performance, rewards/punishments, and motivation, as well as delegation and job satisfaction, provide empirical support for the efficacy of these leadership traits in remote work settings. These findings resonate with prior research emphasizing the significance of transformational leadership in inspiring and motivating employees, alongside the advantages of delegation in empowering staff and augmenting job satisfaction.

Organizations can refine their leadership development and talent management in remote work environments by incorporating these insights into established leadership frameworks. This integration enables them to adapt their approaches to suit remote work arrangements’ unique dynamics and challenges better, fostering enhanced employee engagement, productivity, and satisfaction.

Furthermore, recognizing the importance of these leadership traits can guide organizations in identifying and nurturing influential leaders within their remote workforce. Investing in leadership development programs that cultivate transformational leadership skills and encourage effective delegation can yield long-term benefits regarding employee performance and organizational success in remote work settings.

Additionally, understanding the impact of different leadership styles on remote worker outcomes can inform recruitment and selection processes, enabling organizations to identify candidates with the necessary leadership qualities to thrive in remote roles. This targeted approach to talent acquisition can contribute to building a high-performing remote workforce capable of achieving organizational goals effectively and efficiently.

The findings of this study underscore the critical role of leadership effectiveness in remote work settings and highlight the importance of integrating these insights into organizational practices to maximize remote work success. By leveraging the power of transformational leadership and effective delegation, organizations can create an environment conducive to remote work productivity, satisfaction, and overall well-being.

Strengths and weaknesses of the study. After careful examination of this study,

There will be strengths and weaknesses that have been identified and require critical conversation. One strength of the study was the use of a quantitative, causal-comparative research design to implement this study utilizing de-identified archival data. As such, this study’s selected design will facilitate the comparison of five essential levels of leadership styles commonly associated with working environments in relation to job satisfaction, motivation, and employee satisfaction.

The One-way Multivariate Analysis of Variance (MANOVA), and SPSS data analysis approach will allow valid data processing. The chapter’s discussion on limitations and delimitations expands the discussion in chapter one. The researcher intends to use SurveyMonkey collect participants from across the United States. The instruments used for the study will be comprised of the demographic characteristic’s questions (see Appendix B), the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) also known as Job Satisfaction Index (JSI) (see Appendix I). The tools are going to be considered internally consistent if α are going to be equal to or bigger than 0.7 (Leung, 2001). Four dimensions had an α value greater than 0.65.

The exceptions were item four, concerning Active Management by Exception subscale, with a λ = .17 and, item 17, concerning Passive Management-by Exception subscale, with a λ = .20, showing a reduced contribution for the leadership constructs they represent. Regarding CFA regression weights for the MSLS, all the items presented a λ ≥ .50, ranging from .54 to .91. With no exception, all MSLS items showed a significant contribution for the constructs they represent. Even though the Cronbach’s alpha criteria of two of the MLQ-5X subscales (Management-by-Exception Active and Management-by-Exception Passive) assumed problems of internal consistency, their values were near the acceptable (i.e., α =.687 and α = .696, for this study).

 For the IWPQ subscales, a mean score is going to be calculated by adding the item scores and dividing their sum by the number of items in the subscale. Hence, the IWPQ yields three subscale scores that range between 0 and 4, with higher scores reflecting higher task and contextual performance, and higher counterproductive work behavior. The psychometric properties of the IWPQ have been tested and results indicated good to excellent internal consistency for task performance (α = 0.78), contextual performance (α = 0.85) and counterproductive work behavior (α = 0.79).

While this study offers valuable insights, it will be crucial to acknowledge its limitations. Relying solely on self-reported data may introduce response bias, potentially impacting the validity of the findings. Participants may provide socially desirable responses or misinterpret questionnaire items, leading to inaccuracies. Moreover, employing only close-ended questionnaires as the sole data collection method might oversimplify the nuanced dynamics of remote leadership. Open-ended questions or qualitative interviews could offer deeper insights into the experiences and perceptions of remote workers regarding leadership styles.

Additionally, the study’s exclusive focus on remote workers may restrict the generalizability of its findings to other work environments. Different industries, organizational cultures, and job roles may influence the effectiveness of leadership styles differently (Kim, Lee & Lee, 2021). Therefore, future research endeavors should explore leadership efficacy across diverse organizational contexts, employing mixed methods approaches to enhance comprehensiveness and applicability.

Addressing these limitations can enrich our understanding of leadership dynamics in remote work settings and facilitate the development of more effective management strategies. By incorporating diverse data collection methods, considering broader organizational contexts, and exploring longitudinal effects, researchers can provide deeper insights into the complexities of remote leadership and inform evidence-based practices for enhancing leadership effectiveness in remote work environments.

According to the research findings, remote work setting can be a little difficult when it comes to obtaining the best of each employee’s performance. In addition, the issue of job satisfaction must also go together with employee performance where in this case the chosen leadership style has accounted for all the basic factors contributing to the organization’s success. Therefore, any organization planning to operate remotely must consider various aspects which largely comprise of its core interests, values, and its employee’s needs and wants. For instance, when dealing with remote workers, the organization leadership should be able to provide clear guidance and expectations for each employee. Araz & Azadegan-Mehr (2021) argue that effective leadership style should be able to encourage more employee engagement and can instill more inspiration and motivation towards handling their daily chores. In summary, in any leadership used, the communication channels must be highly effective and flexible in terms of passing on a message. According to Kim, Lee, & Lee, (2021), this aspect can be easily achieved through applying a leadership style that allows individual development of employees while also maintaining a positive work culture.

Remote working is going to be among the latest business models that enable employees to work away from their workstations. Despite the model being in use for just a few years, there is going to be a lot of room for improvement to make sure the model is going to be fully absorbed and incorporated into the organization. The COVID-19 pandemic created a challenge that prompted organizations to adjust their processes so that their employees can work remotely. It was an abrupt scenario that got many organizations unprepared, and they will not know the best way to incorporate the system. For this reason, more research should be done so that the best strategies to utilize remote working will be discovered. There will be different recommendations on what future research should encompass to find the most effective and efficient way to utilize remote working.

1.
Remote Working and Employee Performance

It will be recommended that future research should look at how remote working will ensure the performance of employees will be improved. Employees will be among the most important stakeholders in any organization and their performance determines whether the organization will be successful. Future research needs to tackle issues that will enable organizations to implement remote working and at the same time improve the performance of the employees. For instance, part of the research needs to use employees as respondents and seek their feedback on what they wish should be incorporated into remote working. This will ensure that the strategies and concepts utilized will be relevant and will significantly boost the performance of the employees as they work remotely. Remote employees will be the ones who will be greatly affected by remote working; therefore, their input will be necessary because it will help in making changes that will improve their performance.

2.
Remote Working and Employee Motivation

It’ll be recommended that future research be conducted to find out how remote working can keep employees motivated. Employee motivation refers to the drive and commitment by employees to put in effort to achieve the duties and responsibilities that have been assigned to them. The ability of an employee to perform well largely depends on how motivated they’ll be. Future research should focus on the aspects that can be incorporated into remote work to ensure employees stay committed to their work. The research needs to focus on factors that improve employee motivation and how such factors can be part of the remote working strategy. A successful incorporation of such concepts will enable organizations to achieve employee motivation through remote working. Improvements need to be made from time to time to ensure the level of employee motivation will be always rising.

3.
Remote Working and Employee Satisfaction

Employee satisfaction will be the level of an employee’s contentment with their work. As the world ushers in the era of remote working, it will be important to consider how this move will affect employee satisfaction. As aforementioned, employees will be among the most important stakeholders in any organization because it will be their effort that keeps organizations running. It’ll be recommended that future studies be carried out and focus on how remote working can boost employee satisfaction. The study needs to include employees as respondents so that they can give their suggestions on what they need for them to be content with their work. This will be a move that will ensure relevant aspects will be included in future remote working strategies to make sure employees will be contented.

The world will be going digital due to the advancement in technology. There will be an increasing trend where organizations shall be embracing technology as they focus on improving the efficiency and effectiveness of their business processes and practices. With that in mind, there will be a high chance that remote working will take over in the future; very limited services will be offered inside offices. This implies that future business practices will be digital, and it will be a great opportunity for remote working to thrive. Organizations in the future will no longer be operating the traditional way; they will invest in technology to make sure their services will be available all the time and their employees can work from any place; they do not need to be in the organization’s premises physically. There will be several recommendations for organizations on how they can implement remote working for their future practice.

1.
Avail Technology for Remote Working

Remote working requires a substantial investment in technology. It will be recommended for organizations to provide technology that will be relevant for remote working. Employees who work remotely need personal computers or tablets to do their work. Organizations need to ensure their employees will be provided with the best machines that will enable them to work effectively. In addition to this, organizations should make sure each employee who works remotely will be provided with home Wi-Fi; this will be essential as it will enable them to connect to the internet and enhance communication with other stakeholders. Lastly, technical support should also be availed because sometimes the machines can fail; they need to be maintained properly for them to work well.

2.
Effective Communication

When employees work remotely it means they will not be physically in contact with one another. As much as they shall be provided with all the technology and resources that they need to work remotely, they might feel detached from the rest of the team. For this reason, it’ll be recommended for the organization invest in communication and ensure there will be effective communication with all employees who work remotely. There needs to be frequent communication to enable employees to feel that they will be part of the organization. One of the ways to achieve the recommendation of communication will be to seek feedback from remote employees about matters that affect the organization. As far as working will be concerned, clear guidelines and policies should also be provided to remote employees so that they understand what they should do. Communication is going to be at the center of organizational performance, therefore, for future practice in remote working, organizations need to focus on effective communication.

3.
Offer Social and Emotional Support

Humans will be social and for them to work effectively, they need the social bond of fellow humans. However, with remote working, it will be impossible for employees to physically socialize with one another. If this continues for a long time, it will affect employees, and some might even develop mental health problems. It will be, therefore, recommended for organizations to offer social and emotional support to their employees. They need to organize virtual social sessions that enable employees to interact with one another and share their experiences. The organization also needs a program that focuses on frequently checking on the well-being of the employees. The services of mental health specialists should be sought so that they can help employees stay mentally sound and productive. It will also be the responsibility of senior leaders to support junior employees and to ensure they will be doing well. This will ensure the social and emotional needs of remote employees will be addressed and it will be a move that will potentially help many remote workers in the future to feel like a part of the larger organizational family.

4.
Maintaining Productivity and Engagement

As aforementioned, employees in a remote setting might feel detached if they will not be frequently engaged. It will be the responsibility of leaders to ensure the employees will be engaged as much as possible so that there will be constant contact. It will be recommended for leaders to share best practices that will be related to remote work. This will enable employees to make the best out of their situation. Leaders can also help employees who need help to remain productive. It will be the responsibility of the organization to make sure relevant services will be offered to improve the productivity of the employees. It will also be important to empower employees to work remotely. This involves providing them with tips and techniques that they can apply in their situations to make remote work effective.

This study aimed to investigate the impact of five different leadership styles (structural, participative, servant, freedom-thinking, and transformational) on employee performance, motivation, and job satisfaction in a remote work setting. The study involved 60 participants from various industries who worked remotely. The data was collected through questionnaires and analyzed using descriptive and inferential statistics. The study found that transformational leadership was significantly associated with higher levels of employee performance, servant leadership was significantly associated with higher levels of employee motivation, and participative leadership was significantly associated with higher levels of job satisfaction. However, a freedom-thinking leadership style was found to be associated with lower levels of job satisfaction. The findings of this study provide insights into the importance of selecting appropriate leadership styles for remote work settings and can inform organizational practices that aim to enhance employee outcomes in remote work environments.

Alheet, A., Adwan, A., Areiqat, A., Zamil, A., & Saleh, M. (2021). The effect of leadership styles on employees’ innovative work behavior. 
Management Science Letters, 
11(1), 239-246.

Allred, K. G., Crawford, E. R., David, E. M., &amp; Anderson, L. A. (2018). Freedom-Thinking Leadership in Remote Work Settings: Antecedents and Outcomes. Journal of Leadership &amp; Organizational Studies, 25(2), 160-172.

Amabile, T. M., Schatzel, E. A., Moneta, G. B., & Kramer, S. J. (2004). Leader behaviors and the work environment for creativity: Perceived leader support. Leadership Quarterly, 15(1), 5-32.

Araz, O. M., &amp; Azadegan-Mehr, M. (2021). The impact of participative leadership on team performance, job satisfaction, and motivation in virtual teams. Information &amp; Management, 58(2), 103391.

You are invited to participate in a web-based online survey on The Role of Leadership Styles on Employee Performance, Motivation, and Job Satisfaction in a Remote Setting. This is a research project being conducted by Ameki Williams, a student at South University.  It should take approximately 1-2 minutes to complete.

PARTICIPATION

Your participation in this survey is voluntary. You may refuse to take part in the research or exit the survey at any time without penalty. You are free to decline to answer any particular question you do not wish to answer for any reason.

BENEFITS

You will receive no direct benefits from participating in this research study. However, your responses may help us learn more about whether great forms of leadership truly exist among of strategic management and leadership traits on employee performance, motivation, and job satisfaction in the United States for remote work.

RISKS

There are no foreseeable risks involved in participating in this study other than those encountered in day-to-day life.

CONFIDENTIALITY

Your survey answers will be sent to a link at SurveyMonkey.com where data will be stored in a password protected electronic format. Survey Monkey does not collect identifying information such as your name, email address, or IP address. Therefore, your responses will remain anonymous. No one will be able to identify you or your answers, and no one will know whether you participated in the study.

CONTACT

1. Are you at least the age of 18 through 64?

A. Yes

B. No

2. Are you a remote worker?

A. Yes

B. No

3. Do you have at least 6 months of experience in remote work?

A. Yes

B. No

4. Are you a male or female?

A. Male

B. Female

Leadership Styles for Remote Workers

-This questionnaire is for remote workers to choose which Characteristics best describes which leadership style their supervisors or Managers are.

1. Structural Leader

· Reward and punishes team members based on performance

· Insist on clear goals

· Experiment

2. Servant Leader

· Listens

· Empathy

· Awareness

3. Participative (Democratic) Leader

· Open-Minded

· Encouraging

· Communication

4. Freedom-Thinking Leader

· Give employees freedom to perform

· Stays out the way

· Comments and helps when needed

5. Transformational Leader

· Inspires

· Empowers

· Strong role model

Table 2.
Assumption Strategies for One-Way MANOVA

Scatterplot Metrix

Structural Leadership Participative (Democratic) Leadership Servant Leadership Freedom-Thinking Leadership Transformational Leadership Total 1.4 0.75 0 1 0.96 0.82199999999999995 Structural Leadership Participative (Democratic) Leadership Servant Leadership Freedom-Thinking Leadership Transformational Leadership Total 4 4 3.2 6 3.49 4.1379999999999999 Structural Leadership Participative (Democratic) Leadership Servant Leadership Freedom-Thinking Leadership Transformational Leadership Total 2.742 2.4809999999999999 0.64200000000000002 2.77 2.3742000000000001 2.2018399999999998 Structural Leadership Participative (Democratic) Leadership Servant Leadership Freedom-Thinking Leadership Transformational Leadership Total 0.66900000000000004 0.79890000000000005 6.3780000000000001 1.74 0.49021999999999999 9.6839440000000003 Structural Leadership Participative (Democratic) Leadership Servant Leadership Freedom-Thinking Leadership Transformational Leadership Total 48 48 40 46 56 47

Leadership